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I_kwDOACgets6saqEp
| 4,312
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Selen having a way too low resistance
|
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[
"Hi thanks for asking this, looks like the periodic table JSON recording (with these \"Electrical resistivity\") has been there since the start of pymatgen: https://github.com/materialsproject/pymatgen/blob/24514dd132d371e0f6b9bfbf45faf22a1df427d3/pymatgen/core/periodictable.json\n\nI don't know where the data originally came from, but certainly we would want to update/validate them.\n\nAs far as I could tell, the `periodic_table.json` file provides data as \"value + unit\", and in this specific case, the value for the resistence is \"high\" (you might also see something like \"no data\") and the unit is \"10-8 Ω m\", which is very misleading.\n\nHowever I could confirm the following bug:\n```python\nfrom pymatgen.core import Element\n\nprint(Element.Se.electrical_resistivity) # 1e-08 m ohm\n```\n\nI would look into this and double check the data source."
] | 2025-03-04T01:15:37
| 2025-04-01T15:27:31
|
2025-04-01T15:27:31Z
|
NONE
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## Pymatgen version
Newest
### Current behavior
Resistance of selenium according to pymatgen: "high 10<sup>-8</sup> Ω m",
Resistance of silver according to pymatgen: "high 10<sup>-8</sup> Ω m",
### Expected Behavior
Resistance of selenium being higher or a source where the data is from
### Minimal example
```Python
pymatgen/src/pymatgen/core/periodic_table.json line 12621
```
Thx
|
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I_kwDOACgets6satvA
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More sources/references in periodic table
|
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| 2025-03-04T01:28:40
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NONE
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### Feature Requested
More sources in the periodic table code or perhaps a site with all the sources, so you can put this python library and its sources as valid sources in your paper/essay. An idea would be like that: https://reference.wolfram.com/language/note/ElementDataSourceInformation.html
### Proposed Solution
More sources
Thank you : )
|
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I_kwDOACgets6s29QI
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|
Encoding issues in robocrystallographer output on the website
|
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[
"Hey @fxcoudert , this is probably better placed on the [robocrys](https://github.com/hackingmaterials/robocrystallographer) repo, it is an ongoing issue we're aware of at MP"
] | 2025-03-06T11:04:29
| 2025-03-10T17:15:49
|
2025-03-10T17:15:49Z
|
NONE
|
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### Python version
Python 3.12.9
### Pymatgen version
2025.2.18
### Operating system version
macOS
### Current behavior
See https://next-gen.materialsproject.org/gnome/mp-3206242
which displays for me as:
### Expected Behavior
Correct encoding (UTF-8)
### Minimal example
```Python
```
### Relevant files to reproduce this bug
_No response_
|
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PR_kwDOACgets6NsBEr
| 4,315
|
patch to allow pyzeo to work
|
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[] | null |
[
"Great thanks. Can you add / enable some unittests to check that this is working? Thanks.",
"You would also need to add it as an [optional dependency](https://github.com/materialsproject/pymatgen/blob/master/pyproject.toml), thanks for the edit!",
"Note that even with the patch one would still need to manually install zeo++, i've added some logic to the test.yaml in the workflow for this, but can't really test it on my end. The tests pass if zeo++ is installed. Historically testing the zeo++ plugin was just skipped for this reason, but maybe it can be automated and included? ",
"@shyuep @mkhorton a gentle reminder to take a look whenever you have time. -Daniel ",
"Thanks!",
"Thanks everyone! "
] | 2025-03-06T20:04:24
| 2025-03-10T14:23:48
|
2025-03-10T13:59:04Z
|
CONTRIBUTOR
|
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Hi everyone,
Pyzeo is a more maintained version of the cython but has different imports. This small patch allows users to swap out zeo++ with pyzeo.
|
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I_kwDOACgets6s_T70
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Error when using CifParser to read 0.220_Mn2SiO4.mcif
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[
"Thanks for reporting and I could recreate this. We globally enabled `strict` for `zip` at some point to ensure equal-length iterables and apparently test might be missing at certain spots :)",
"@Connor1y I just had a closer look and not sure if the cif file format is valid (**I could certainly be wrong here as I'm not super familiar with [CIF file synax](https://www.iucr.org/resources/cif/spec/version1.1/cifsyntax#general), CIF experts please feel free to comment**).\n\nThe cif file has the following tabular data:\n```text\nloop_\n_irrep_id\n_irrep_dimension\n_irrep_small_dimension\n_irrep_direction_type\n_irrep_action\n_irrep_modes_number\n_irrep_presence\nmGM2+ 1 1 . primary 5 .\n\n;\nNSD\nMoment moduli: 3.85(11) for Co1 and 4.68(9) for Co2\nMagnetic structures, similar but with different moments, are reported in the same reference for T=4K and 20K\n;\n\n```\n\nWhere 7 headers map to 8 values with an extra multi-line string at the end, and therefore the exception (with strict `zip`).\n\n ",
"Thanks for your response.\n\nIt's indeed an issue with the file itself. It lacks \n\n_exptl_crystal_magnetic_properties_details\n\nbefore the \n\n;\nNSD",
"Thanks for confirming! Yes it looks like the data name (tag) is missing to me"
] | 2025-03-07T07:52:07
| 2025-03-10T10:07:07
|
2025-03-10T09:36:37Z
|
NONE
|
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### Python version
Python 3.12.7
### Pymatgen version
2025.2.18
### Operating system version
MacOS15.1 (24B2082)/CentOS Linux 7 (Core)
### Current behavior
when using CifParser to read 0.220_Mn2SiO4.mcif from [MAGNDATA](https://www.cryst.ehu.es/magndata/index.php?this_label=0.220),
we got this error
pymatgen/src/pymatgen/io/cif.py", line 233, in from_str
for k, v in zip(columns * n, items, strict=True):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: zip() argument 2 is longer than argument 1
but in version pymatgen 2023.9.25, nothing wrong with that
### Expected Behavior
read the information of .mcif correctly
### Minimal example
```Python
from pymatgen.io.cif import CifParser
file = CifParser(r"0.220_Mn2SiO4.mcif")
```
### Relevant files to reproduce this bug
_No response_
|
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I_kwDOACgets6tDQhg
| 4,318
|
Suspected little error in: pymatgen/transformations/advanced_transformations.py
|
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[] | 2025-03-07T15:32:29
| 2025-03-23T09:17:18
|
2025-03-23T09:17:18Z
|
NONE
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### Python version
3.12.4
### Pymatgen version
2024.10.3
### Operating system version
_No response_
### Current behavior
In the class EnumerateStructureTransformation (defined in pymatgen/transformations/advanced_transformations.py), the M3GNetCalculator is called like this:
m3gnet_model = M3GNetCalculator(potential=potential, stress_weight=0.01).
However, the stress_weight value should be either 1.0 (for GPa) or 1 / 160.21766208 (for eV/A^3).
### Expected Behavior
Either:
m3gnet_model = M3GNetCalculator(potential=potential). (--> default value of 1.0 would be used),
or:
m3gnet_model = M3GNetCalculator(potential=potential, stress_weight=1/160.21766208).
### Minimal example
```Python
Since it is not a bug but an eventual little mistake, no code snippet is provided.
```
### Relevant files to reproduce this bug
Since it is not a bug but an eventual little mistake, no file is provided.
|
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PR_kwDOACgets6OANnk
| 4,319
|
Update abitimer in io.abinit
|
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[] | 2025-03-10T14:19:21
| 2025-03-10T14:41:07
|
2025-03-10T14:41:07Z
|
CONTRIBUTOR
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Parsing of the timer section has stopped working for the latest versions of Abinit. In addition the generation of a dataframe in the same set of objects is incompatible with pandas>2.
In this PR
* fixing these issues
* added a test file generated by latest abinit version (10.2.7)
* added tests for the abitimer module.
Pinging @gmatteo in case he has any comment.
|
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I_kwDOACgets6tYxH6
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`Molecule.from_ase_atoms()` does not pass kwargs appropriately
|
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[
"I'm happy to look into this :)"
] | 2025-03-11T03:17:37
| 2025-03-17T13:41:01
|
2025-03-17T13:41:01Z
|
MEMBER
|
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### Python version
3.12
### Pymatgen version
2025.3.10
### Operating system version
_No response_
### Current behavior
The `Molecule.from_ase_atoms` method does not accept dedicated keyword arguments for the `Molecule` class. I demonstrate this below:
```python
from ase.build import molecule
from pymatgen.core import Molecule
from pymatgen.io.ase import AseAtomsAdaptor
atoms = molecule("CH3")
Molecule.from_ase_atoms(atoms, charge_spin_check=False)
```
The above returns:
```
TypeError: Structure.__init__() got an unexpected keyword argument 'charge_spin_check'
```
However, the following works as expected:
```python
from ase.build import molecule
from pymatgen.io.ase import AseAtomsAdaptor
atoms = molecule("CH3")
AseAtomsAdaptor.get_molecule(atoms, charge_spin_check=False)
```
This is not a hard patch. I personally can't fix it at the moment though. PRs welcome!
|
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PR_kwDOACgets6OJX7v
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[Breaking] `from_ase_atoms` constructor for `(I)Structure/(I)Molecule` returns the corresponding type
|
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[
"This looks good to me. I will give this through the end of the week for others to chime in, and if there's nothing I will do a final review and merge over the weekend.",
"Thanks, @DanielYang59! Merging. ",
"Ah, looks like I don't have permission to merge until the CI passes... ",
"> Ah, looks like I don't have permission to merge until the CI passes...\r\n\r\nNo rush at all! I'm working on fixing these new linting errors in #4327\r\n",
"Merged",
"Thanks!"
] | 2025-03-11T11:35:17
| 2025-03-17T13:46:04
|
2025-03-17T13:41:00Z
|
CONTRIBUTOR
|
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### Summary
- Fix `from_ase_atoms` for `Molecule`, to close #4320
- [x] Add tests
|
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Tweak POSCAR / XDATCAR to accommodate malformed files
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[
"Thanks."
] | 2025-03-13T18:22:51
| 2025-03-13T19:33:54
|
2025-03-13T19:33:49Z
|
CONTRIBUTOR
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Related to [this matsci.org issue](https://matsci.org/t/xdatcar-error-when-reading-xdatcar-could-not-convert-string-to-float/61831): sometimes the XDATCAR can be malformed because fortran uses fixed format floats when printing. In those cases, there's no space between coordinates:
```
Direct configuration= 2
-0.63265286-0.11227753 -0.15402785
-0.12414874 -0.01213420 -0.28106824
...
```
In some cases, this issue is reparable (a negative sign separates coordinates). This PR implements a fix when it is reparable and adds a few Trajectory-like convenience features to `Xdatcar`
|
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GibbsComputedStructureEntry update to handle float temperature values
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[
"Thanks,."
] | 2025-03-14T02:37:46
| 2025-04-20T19:38:20
|
2025-04-20T19:38:20Z
|
CONTRIBUTOR
|
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## Summary
Issue: when temperature %100==0 but is type float, you get KeyError because G_ELEMS has keys of integer values.
Major changes:
- feature 1: updated to handle float temperature values
- fix 1: G_ELEMS[str(self.temp)] is changed to G_ELEMS[str(int(self.temp))]
## Checklist
- [ ] Google format doc strings added. Check with `ruff`.
- [ ] Type annotations included. Check with `mypy`.
- [ ] Tests added for new features/fixes.
- [ ] If applicable, new classes/functions/modules have [`duecredit`](https://github.com/duecredit/duecredit) `@due.dcite` decorators to reference relevant papers by DOI ([example](https://github.com/materialsproject/pymatgen/blob/91dbe6ee9ed01d781a9388bf147648e20c6d58e0/pymatgen/core/lattice.py#L1168-L1172))
Tip: Install `pre-commit` hooks to auto-check types and linting before every commit:
```sh
pip install -U pre-commit
pre-commit install
```
|
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I_kwDOACgets6uCuaK
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LATTICE_CONSTRAINTS tag not read properly for Incar
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[
"Hi @edansi thanks for reporting this. \n\nLooks like the type handling mechanism of INCAR tags has really been a pain and I assume it's time we really think about a more permanent solution other than having people reporting certain tag is not parsed correctly from time to time. \n\nUnfortuntately currently AFAIK the VASP manual doesn't provide any programmatic interface to retrieve expected type for certain tags and we're largely manually maintaining the dynamic and huge list INCAR tags. I would reach out to them and see if it's possible to provide a better way to handle this (otherwise a crawler might be necessary).\n- https://github.com/materialsproject/pymatgen/issues/4119\n\nThanks for the report in any case. \n\nUpdate (26 March): Currently working on this",
"@yantar92 Do we have any progress in getting an API from the VASP wiki?\n\nI tried to open a discussion in VASP forum but looks like it requires to be within a research group holding a valid VASP license to register for the forum. As I just moved to a new institute, I haven't figured out how VASP license works from my side.",
"@DanielYang59 there is py4vasp; after discussing with VASP developers, it makes the most sense to use [p4vasp-core](https://github.com/vasp-dev/py4vasp/tree/master/core) to parse newer I/O coming from VASP. This only adds an h5py dependence to pymatgen\n\nThis will ensure, e.g., that vaspout.h5, vaspwave.h5, and vaspin.h5 is up to date with schema changes. It should also help with INCAR parsing, since these are core components of vaspout and vaspin as well",
"Hi @esoteric-ephemera thanks for the input! I haven't used py4vasp before but that sounds like a great idea to handover this to VASP developers, so do you want to take over or allow me some time to explore that?\n\nAlso I just had a quick try and it looks like adding `py4vasp` would bring in quite a lot additional dependencies though:\n\n```\n>>> pip install pymatgen # only the base pmg without optional\n\n>>> pip install --dry-run py4vasp\n\nWould install Pygments-2.19.1 Send2Trash-1.8.3 anyio-4.9.0 appnope-0.1.4 argon2-cffi-23.1.0 \nargon2-cffi-bindings-21.2.0 arrow-1.3.0 ase-3.24.0 asttokens-3.0.0 async-lru-2.0.5 babel-2.17.0\n beautifulsoup4-4.13.3 bleach-6.2.0 comm-0.2.2 debugpy-1.8.13 decorator-5.2.1 defusedxml-0.7.1\n executing-2.2.0 fastjsonschema-2.21.1 fqdn-1.5.1 h11-0.14.0 h5py-3.13.0 httpcore-1.0.7 \nhttpx-0.28.1 ipykernel-6.29.5 ipython-8.34.0 ipywidgets-8.1.5 isoduration-20.11.0 jedi-0.19.2\n json5-0.12.0 jsonpointer-3.0.0 jupyter-events-0.12.0 jupyter-lsp-2.2.5 jupyter_client-8.6.3 \njupyter_core-5.7.2 jupyter_server-2.15.0 jupyter_server_terminals-0.5.3 jupyterlab-4.3.6 jupyterlab_pygments-0.3.0 jupyterlab_server-2.27.3 jupyterlab_widgets-3.0.13 kaleido-0.2.1 \nmatplotlib-inline-0.1.7 mistune-3.1.3 nbclient-0.10.2 nbconvert-7.16.6 nbformat-5.10.4 \nnest-asyncio-1.6.0 nglview-3.1.4 notebook-7.3.3 notebook_shim-0.2.4 overrides-7.7.0 \npandocfilters-1.5.1 parso-0.8.4 pexpect-4.9.0 platformdirs-4.3.7 prometheus_client-0.21.1 \nprompt_toolkit-3.0.50 psutil-7.0.0 ptyprocess-0.7.0 pure_eval-0.2.3 py4vasp-0.10.1 \npython-json-logger-3.3.0 rfc3339-validator-0.1.4 rfc3986-validator-0.1.1 sniffio-1.3.1 \nsoupsieve-2.6 stack-data-0.6.3 terminado-0.18.1 tinycss2-1.4.0 tornado-6.4.2 traitlets-5.14.3 \ntypes-python-dateutil-2.9.0.20241206 uri-template-1.3.0 wcwidth-0.2.13 webcolors-24.11.1 \nwebencodings-0.5.1 websocket-client-1.8.0 widgetsnbextension-4.0.13\n```",
"@DanielYang59 we would specifically use `py4vasp-core`, which only adds h5py to pymatgen's dependencies. `py4vasp-core` was developed for ASE, where the devs wanted minimal overhead from VASP I/O\n\nI'd like to write the initial draft. @shyuep is this plan OK with you? \n\nFrom a practical standpoint, we can't keep up to date with schema changes while py4vasp is still in alpha. In writing the vaspout.h5 parser, I found breaking schema changes in a few areas so I think it's best we don't lag behind these"
] | 2025-03-14T11:20:22
| 2025-04-03T17:23:53
|
NONE
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### Python version
3.11
### Pymatgen version
2025.1.9
### Operating system version
_No response_
### Current behavior
For the newer Vasp version, there's a new tag LATTICE_CONSTRAINT (list of three booleans) that allows the relaxation of only some of the lattice parameters. When reading an Incar file with this tag, it will only output the first of the three booleans, e.g. the input `LATTICE_CONSTRAINTS = True False False` is changed to `LATTICE_CONSTRAINTS = True` in the Incar object. When Vasp is run with e.g. atomate2 and custodian wants to restart a job that has this tag, Vasp will fail after the restart because it is missing two of the three required booleans.
### Expected Behavior
I solved the problem for my calculations by adding a new boolean list `bool_lists` in the `proc_val` method of the Incar class and separately reading in the `LATTICE_CONSTRAINTS` as a list instead of a single value:
```
if key in bool_lists:
output = []
tokens = re.findall(r"\b(?:True|False)\b", val, re.IGNORECASE)
for tok in tokens:
output.extend([tok[0].lower() == "t"])
return output
```
### Minimal example
```Python
```
### Relevant files to reproduce this bug
_No response_
|
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I_kwDOACgets6uEgEs
| 4,326
|
[RFC] Upstreaming methods for prototype assignment and protostructure assignment to pymatgen.
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[
"Resolved by #4329 "
] | 2025-03-14T14:35:45
| 2025-03-25T22:00:57
|
2025-03-25T22:00:55Z
|
CONTRIBUTOR
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For my `wren` work I wrote several methods that use `spglib` to get prototype labels that follow the `aflow-sym` assignment scheme. These are generally more useful than just for the `wren` model, for example afaik @lbluque used them to help downselect materials to include in the OMAT dataset.
Installing my `aviary` package to access these utilities creates dependency sprawl and so it makes sense to me to put them somewhere like `pymatgen` where basically everyone already will have it as a dependency.
The code is mostly functional rather than object oriented and does require the addition of some small data files. Essentially it would be rehoming most of this utils file into pymatgen:
https://github.com/CompRhys/aviary/blob/main/aviary/wren/utils.py
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Fix new `ruff` error in linting pipeline
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[] | 2025-03-15T08:58:40
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2025-03-17T13:39:07Z
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CONTRIBUTOR
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## Summary
- Fix new `ruff` error in linting pipeline
- Avoid configuring the root logger
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Remove legacy MPRester method tests from `TestMPResterNewBasic`
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[
"> ping @ml-evs for review?\r\n\r\nDon't think this is anything to do with me (nor does my review do anything on this repo), but generally looks fine...",
"Ooops sorry thought you're the owner of this module but thanks for looking into this in any case :) \r\n\r\nhttps://github.com/materialsproject/pymatgen/blob/eec44223101d24c690e001ff3cf8d3dfa00e7e02/.github/CODEOWNERS#L5-L6",
"> Ooops sorry thought you're the owner of this module but thanks for looking into this in any case :)\r\n> \r\n> https://github.com/materialsproject/pymatgen/blob/eec44223101d24c690e001ff3cf8d3dfa00e7e02/.github/CODEOWNERS#L5-L6\r\n\r\nAh yes, I remember -- I only wanted to be CODEOWNER on the OPTIMADE tests, and as we discovered in that PR CODEOWNER doesn't really mean a lot if you don't have write access (you don't automatically get pinged). Either way, happy to approve ;)",
"> Either way, happy to approve ;)\r\n\r\nThanks! Just want to get extra eyes on this in case I'm removing some still needed code (unlikely in this case as all these were commented out before) now that MP API is separatedly maintained \r\n"
] | 2025-03-15T13:02:12
| 2025-03-15T15:49:01
|
2025-03-15T15:40:52Z
|
CONTRIBUTOR
|
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### Summary
Remove legacy MPRester tests from `TestMPResterNewBasic`, I believe some test methods (commented out previously, and recovered in #4027) are testing methods from the legacy MPRester (while being inside `TestMPResterNewBasic`), maybe we should remove them altogether?
ping @ml-evs for review?
|
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|
Add protostructure and prototype functions from aviary
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[
"Closes https://github.com/materialsproject/pymatgen/issues/4326",
"@mkhorton "
] | 2025-03-17T01:10:48
| 2025-03-26T00:43:10
|
2025-03-22T15:48:51Z
|
CONTRIBUTOR
|
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## Summary
Adds functions to get protostructure labels from spglib, moyo and aflow-sym. This avoids users who wish to use this functionality from needing to download `aviary` [to use these functions](https://github.com/CompRhys/aviary/blob/main/aviary/wren/utils.py).
## TODO
* ~~should `pyxtal` be added to the "symmetry" deps?~~ DONE
* ~~not sure why the file for original prototype matcher is marked as deleted given file was renamed and then functions were added. Check if blame correctly preserved.~~ YES - https://github.com/CompRhys/pymatgen/blame/protostructure/src/pymatgen/analysis/prototypes/__init__.py
* ~~compress the json files to reduce line count inflation. Should prototypes also be compressed?~~ DONE
## Checklist
- [x] Google format doc strings added. Check with `ruff`.
- [x] Type annotations included. Check with `mypy`.
- [x] Tests added for new features/fixes.
- [x] If applicable, new classes/functions/modules have [`duecredit`](https://github.com/duecredit/duecredit) `@due.dcite` decorators to reference relevant papers by DOI ([example](https://github.com/materialsproject/pymatgen/blob/91dbe6ee9ed01d781a9388bf147648e20c6d58e0/pymatgen/core/lattice.py#L1168-L1172))
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PR_kwDOACgets6O2fbY
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Update type-hinting for `pymatgen.io.qchem.outputs`
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[
"I guess we have a type alias `PathLike` which might be preferred to annotate such \"pathlike\" things: https://github.com/materialsproject/pymatgen/blob/168999dc7acac4f4dda6296b5cef1ca512ed33a6/src/pymatgen/util/typing.py#L28\r\n\r\n```python\r\nfrom pymatgen.util.typing import PathLike\r\n```"
] | 2025-03-17T04:12:00
| 2025-03-17T13:38:34
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2025-03-17T13:38:32Z
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## Summary
I updated several I/O parsers in `pymatgen.io.qchem.outputs` such that they are properly type-hinted with `str | Path` arguments rather than just `str`.
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PR_kwDOACgets6PjbBv
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Optimized cube file parsing in from_cube for improved speed
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[
"Nice! Thanks a lot!"
] | 2025-03-21T03:07:49
| 2025-03-22T16:35:11
|
2025-03-22T16:35:11Z
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CONTRIBUTOR
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# **Summary**
### **Major Changes:**
This PR enhances the `from_cube` function in `io.common.VolumetricData` to significantly improve performance. When processing large `.cube` files, the original implementation took minutes to read and set Pymatgen objects. The optimized version incorporates several key improvements: file reading is now handled with `readlines()` instead of multiple `readline()` calls, reducing I/O operations. Voxel data parsing has been rewritten to use NumPy vectorized parsing instead of loops, making numerical processing faster. Atom site parsing has been improved by replacing the loop-based `readline()` approach with list comprehensions. Additionally, volumetric data parsing now leverages `np.fromstring()` instead of converting lists to NumPy arrays.
### **Performance Improvements**
The Figures below illustrate the execution time and memory usage improvements:
**Figure 1**: Performance comparison for large `.cube` files (GB-scale).
<img src="https://github.com/user-attachments/assets/510e123f-e943-4ebf-9647-8743a20b8a03" width="600">
**Figure 2**: Performance comparison for small `.cube` files (MB-scale).
<img src="https://github.com/user-attachments/assets/8695957b-c737-4312-ae14-777d947df213" width="600">
To quantify improvements, I benchmarked 13 `.cube` files of various structures, volumetric data, and sizes. The optimized `from_cube()` is 55-65% faster in parsing while maintaining comparable memory usage.
Interestingly, preloading the entire file did not significantly impact RAM usage. While the optimized version appears to use slightly more RAM for smaller files (~20-100 MB difference), this is negligible given the short allocation time.
For File 3, a high memory usage difference was observed across multiple runs. While I am unsure of the exact cause, it might be related to NumPy's internal memory management and temporary storage allocation when parsing large files.
---
# **To-Do**
Simple upgrade completed.
_Note_: All tested `.cube` files were generated from GCMC, probability plots (aka density plots). The improvements should be applicable to DFT-generated `.cube` files all the same (electron densities, molecular orbitals).
---
# **Checklist**
[Y] Google format doc strings added. Check with `ruff`.
Formatting & linting checks passed (`pre-commit run --all-files` results below):
```shell
ruff.....................................................................Passed
ruff-format..............................................................Passed
check yaml...............................................................Passed
fix end of files.........................................................Passed
trim trailing whitespace.................................................Passed
mypy.....................................................................Passed
codespell................................................................Passed
cython-lint..............................................................Passed
double-quote Cython strings..............................................Passed
blacken-docs.............................................................Passed
markdownlint.............................................................Passed
nbstripout...........................................(no files to check)Skipped
pyright..................................................................Passed
```
[Y] Type annotations included. Check with `mypy`.
Since only optimizations were made, type annotations remain unchanged from the original function.
[Y] Tests added for new features/fixes.
No new tests were added specifically, but I verified that the changes did not break anything by running pytest. The results were identical to those obtained on a fresh fork:
```shell
4 failed, 2973 passed, 161 skipped, 103 warnings
```
I also developed a local `pytest` to validate that the optimized `from_cube()` generates identical results as the original.
### Tested parameters:
- Lattice parameters
- Structure equality
- Volume consistency
- Volumetric data integrity
- Atomic species and coordinates
- Density statistics
- Lattice angles
- Structure symmetry
All tests passed across 13 benchmark `.cube` files.
---
This is my first contribution, so let me know if I'm missing anything!
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Bump nokogiri from 1.18.3 to 1.18.4 in /docs
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[] | 2025-03-22T00:00:26
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2025-03-22T15:26:35Z
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Bumps [nokogiri](https://github.com/sparklemotion/nokogiri) from 1.18.3 to 1.18.4.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/sparklemotion/nokogiri/releases">nokogiri's releases</a>.</em></p>
<blockquote>
<h2>v1.18.4 / 2025-03-14</h2>
<h3>Security</h3>
<ul>
<li>[CRuby] Vendored libxslt is updated to <a href="https://gitlab.gnome.org/GNOME/libxslt/-/releases/v1.1.43">v1.1.43</a> to address CVE-2025-24855 and CVE-2024-55549. See <a href="https://github.com/sparklemotion/nokogiri/security/advisories/GHSA-mrxw-mxhj-p664">GHSA-mrxw-mxhj-p664</a> for more information.</li>
</ul>
<!-- raw HTML omitted -->
<pre><code>8f2263cef9953ce09bd5293d76c9bbd3013d2f94d1cca67783dfe6635c529deb nokogiri-1.18.4-aarch64-linux-gnu.gem
4e231f8ba3128cfc2ef0cc0bdc807d7ce71fc62cb6a78216e817be8631fe6a96 nokogiri-1.18.4-aarch64-linux-musl.gem
73902663b23b1123282b9c0b6d9654b1fb286dfee8d65cb1f6029087b7f0d037 nokogiri-1.18.4-arm64-darwin.gem
cc2945e2c19560a61a97737e6bd3b329edb1f82ca204d46a18e5e98ad0a550a6 nokogiri-1.18.4-arm-linux-gnu.gem
4fb7f44de0cd85abfa869e4cfb619410da174ebf9fbe26ae0caa65462b818bcb nokogiri-1.18.4-arm-linux-musl.gem
bb7820521c1bbae1d3e0092ff03b27a8e700912b37d80f962b7e4567947a64ac nokogiri-1.18.4.gem
cbc0bab72eb5a9573efa7b98351fdd44c609e8d4585456ca1be18db2b7764b64 nokogiri-1.18.4-java.gem
bd567cb509eb75de8f27ca6ecaf4a38bf0563482188991f9bcccccac9c3b9a2f nokogiri-1.18.4-x64-mingw-ucrt.gem
e4776f58eea9b94d05caf8bf351e3c6aa1cce01edcc2ed530f3c302c13178965 nokogiri-1.18.4-x86_64-darwin.gem
b1c6407b346b88704e97a342a80acd4755175324e624da34d0c5cfdc8d34191e nokogiri-1.18.4-x86_64-linux-gnu.gem
ea7c0356a70f3d2d0d76315b533877013d20368d5c9f437c38e0bd462c4844dc nokogiri-1.18.4-x86_64-linux-musl.gem
</code></pre>
</blockquote>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/sparklemotion/nokogiri/blob/main/CHANGELOG.md">nokogiri's changelog</a>.</em></p>
<blockquote>
<h2>v1.18.4 / 2025-03-14</h2>
<h3>Security</h3>
<ul>
<li>[CRuby] Vendored libxslt is updated to <a href="https://gitlab.gnome.org/GNOME/libxslt/-/releases/v1.1.43">v1.1.43</a> to address CVE-2025-24855 and CVE-2024-55549. See <a href="https://github.com/sparklemotion/nokogiri/security/advisories/GHSA-mrxw-mxhj-p664">GHSA-mrxw-mxhj-p664</a> for more information.</li>
</ul>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/sparklemotion/nokogiri/commit/80edf1c35d79eae3a2fd7315f1fb61831ca3c94a"><code>80edf1c</code></a> version bump to v1.18.4</li>
<li><a href="https://github.com/sparklemotion/nokogiri/commit/3c12e357632043725bb7dde0e8a9992d08c66b57"><code>3c12e35</code></a> dep: update libxslt to 1.1.43 (v1.18.x branch) (<a href="https://redirect.github.com/sparklemotion/nokogiri/issues/3467">#3467</a>)</li>
<li><a href="https://github.com/sparklemotion/nokogiri/commit/395c085edd7bd00bfb6b36b23a0ff2f9b8e29d30"><code>395c085</code></a> dep: update libxslt to 1.1.43</li>
<li><a href="https://github.com/sparklemotion/nokogiri/commit/41257151137222136b4af1faa75ba9bf2fbd5ad2"><code>4125715</code></a> ci: tired of waiting for gnome mirrors</li>
<li><a href="https://github.com/sparklemotion/nokogiri/commit/35faaa3fcb4a726bfb8d5e5475b89247909fa0a2"><code>35faaa3</code></a> doc: update CHANGELOG with GHSA</li>
<li>See full diff in <a href="https://github.com/sparklemotion/nokogiri/compare/v1.18.3...v1.18.4">compare view</a></li>
</ul>
</details>
<br />
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Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`.
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I_kwDOACgets6vRKqt
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Sort a molecular crystal supercell
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[
"I would use the extract_cluster method. Just extract, iterate, extract until all sites are bundled into separate clusters. I personally think this is always the easiest way rather than trying to do complicated graph-based algorithms."
] | 2025-03-22T17:17:50
| 2025-04-20T19:59:07
|
2025-04-20T19:59:07Z
|
NONE
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I want to sort a molecular crystal supercell cif so the atoms are printed one molecule after the other in any file either cif or pdb. I was trying with a neighbor search. If you could suggest a better way to sort the molecules?
```
from pymatgen.core.structure import Structure, Molecule
from pymatgen.analysis.graphs import StructureGraph
from pymatgen.analysis.local_env import JmolNN
molecules = Structure.from_file("supercell.cif")
sg = StructureGraph.with_local_env_strategy(molecule, JmolNN())
molecules = sg.get_subgraphs_as_molecules()
```
Here the molecules have the atoms for one molecule.
|
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PR_kwDOACgets6QFnF7
| 4,334
|
Updated Potentials Class in FEFF io to consider radius
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[
"Thanks very much. Can you add some tests to test_feff.py for the updated functionality? ",
"I've added a test, and I've changed the behavior of the Potentials class to be able to handle situations where the radius is not defined (in which case it will revert to the old behavior of defining a potential for every atom in the structure).",
"Thanks!",
"Thanks. Merged"
] | 2025-03-25T17:57:35
| 2025-03-28T19:35:05
|
2025-03-28T19:02:10Z
|
CONTRIBUTOR
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Changed the Potentials class to consider the same radius that is used in
the Atoms class. This is necessary to avoid a situation where the radius is small enough to only have a subset of the unique elements in a structure, but all the elements have potentials defined, which causes FEFF to fail when run.
## Summary
Major changes:
- fix 1: **Previous behavior**: When creating a FEFFDictset using the feff.io tools, the potentials class defined a potential for every unique element in a structure, while the atoms class defined an atom coordinate for every atom in a radius around the absorbing atom. If the radius was defined to be small, only a subset of the unique atoms in the structure would be included in the atom class. **New behavior**: Updated the potentials class to only create potentials for atoms inside the radius specified when creating a FEFFDictset. Without this, a too small radius for a structure with unique elements outside of that radius would cause FEFF to fail, given that there was a Potential defined for an element that did not exist in the Atoms flag.
## Todos
None, the work is complete
## Checklist
- [ ] Google format doc strings added. Check with `ruff`.
- [ ] Type annotations included. Check with `mypy`.
- [ ] Tests added for new features/fixes.
- [ ] If applicable, new classes/functions/modules have [`duecredit`](https://github.com/duecredit/duecredit) `@due.dcite` decorators to reference relevant papers by DOI ([example](https://github.com/materialsproject/pymatgen/blob/91dbe6ee9ed01d781a9388bf147648e20c6d58e0/pymatgen/core/lattice.py#L1168-L1172))
Tip: Install `pre-commit` hooks to auto-check types and linting before every commit:
```sh
pip install -U pre-commit
pre-commit install
```
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I_kwDOACgets6wKdv6
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|
Incorrect MAGMOM Handling in MPStaticSet When Using add_spin_by_site()
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[] | 2025-03-28T09:32:39
| 2025-03-28T09:40:43
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NONE
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### Python version
Python 3.13.2
### Pymatgen version
2024.10.3
### Operating system version
Ubuntu 24.04
### Current behavior
When adding magnetic moments to a structure, `MPStaticSet` produces different `MAGMOM` values in the INCAR depending on whether `add_site_property()` or `add_spin_by_site()` is used. The latter method appears to flip the signs of the magnetic moments.
-
- Using `add_site_property()` produces correct `MAGMOM` values in INCAR
- Using `add_spin_by_site()` flips the signs of magnetic moments in the resulting INCAR
### Expected Behavior
Both methods should produce identical `MAGMOM` values in the INCAR file when given identical spin configurations.
### Minimal example
```Python
from numpy.testing import assert_allclose
from pymatgen.core import Structure
from pymatgen.io.vasp.sets import MPStaticSet
EXPECTED_MAGMOM = [5.0, -5.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
def demo_add_magmom_and_write_incar():
structure = Structure.from_file(filename='POSCAR')
spins = [+5, -5, 0, 0, 0, 0, 0, 0, 0, 0]
structure.add_site_property("magmom", spins)
mp_static_set = MPStaticSet(structure=structure)
incar_dict = mp_static_set.incar.as_dict()
assert_allclose(incar_dict.get('MAGMOM'), EXPECTED_MAGMOM) # Passes
def demo_add_spin_and_write_incar():
structure = Structure.from_file(filename='POSCAR')
spins = [+5, -5, 0, 0, 0, 0, 0, 0, 0, 0]
structure.add_spin_by_site(spins)
mp_static_set = MPStaticSet(structure=structure)
incar_dict = mp_static_set.incar.as_dict()
assert_allclose(incar_dict.get('MAGMOM'), EXPECTED_MAGMOM) # Fails (signs flipped)
if __name__ == "__main__":
demo_add_magmom_and_write_incar()
demo_add_spin_and_write_incar()
```
### Relevant files to reproduce this bug
POSCAR file:
```
MnPSe3
6.40804163940561
0.8660254037844390 -0.5000000000000000 0.0000000000000000
0.8660254037844390 0.5000000000000000 0.0000000000000000
0.0000000000000000 0.0000000000000000 3.7182201236084644
Mn P Se
2 2 6
Selective dynamics
Direct
0.0000000000000000 0.0000000000000000 0.0000000000000000 F F F
0.3333333333333357 0.3333333333333357 0.0000000000000000 F F F
0.6666666666666643 0.6666666666666643 0.0470578253908371 T T T
0.6666666666666643 0.6666666666666643 0.9529421746091629 T T T
0.6666666666666643 0.3356524591118557 0.0732317250636854 T T T
0.9976808742214800 0.6666666666666643 0.0732317250636854 T T T
0.3356524591118557 0.9976808742214800 0.0732317250636854 T T T
0.3356524591118557 0.6666666666666643 0.9267682749363146 T T T
0.9976808742214800 0.3356524591118557 0.9267682749363146 T T T
0.6666666666666643 0.9976808742214800 0.9267682749363146 T T T
```
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I_kwDOACgets6wNRpR
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[Proposal] Better periodic table dev recording (`periodic_table.yaml`)- One property per file?
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[] | null |
[
"Maintenance is not done on the json, but on the yaml (which is a lot easier to read and modify). That file is in dev scripts. \nI am not opposed to splitting up by element but I suspect 100 file open calls will be more expensive than loading one big file. If you can prove to me that loading multiple element data files is just as fast as loading one big file, I am willing to consider it.\n\nThe way the periodic_table.json should be thought of is like the \"min\" versions of javascript for webpages, as opposed to the non-min version that is used in development. The reason why you use the min version, which is difficult to read and edit, is that it loads faster.\n\nElement is something that must be loaded by default in pymatgen. I don't think there is a situation where elemental data is not loaded.",
"\n\nThis is what the YAML looks like in the PyCharm IDE. Navigation to a different element is trivial based on the file structure and updating a value is super easy. I added a comment in the periodic_table.py to make sure people understand how the data is updated.",
"> I am not opposed to splitting up by element but I suspect 100 file open calls will be more expensive than loading one big file. If you can prove to me that loading multiple element data files is just as fast as loading one big file, I am willing to consider it.\n\nI agree loading multiple small files might very likely be slower than one big file, I would need some test on my side. In the worst case scenario though, we might just do one file per property yaml file during development, and then drop the metadata and concatenate all yaml files into a single json recording for production?\n\n> Element is something that must be loaded by default in pymatgen. I don't think there is a situation where elemental data is not loaded.\n\nI was trying to say we could selectively load certain property (i.e. only load \"atomic radius\"), instead of don't load elemental data at all",
"ISn't that what is done already? I don't see how navigating the yaml file today is difficult. You can click on the project structure or even just do Cmd+F Se to get to Selenium.",
"I wasn't suggesting finding one property for a specific element is difficult, rather maintaining certain property for all elements might be tricky (esp. some property might lack programmatic pipe to retrieve, i.e. one might need to get certain property for all elements from a handbook and update/verify current recording). \n\nAnd in current format, we might be lossing importable metadata for example source of each data entry and so on?\n\n",
"The current file can be updated with meta-data by adding a new dict key. The original PT data was generated from a reasonable source and certainly we can check the values from time to time. Even if we do it manually, what is really difficult about updating it from the current yaml?\n\nI just don't want to overengineer a solution that is barely going to be used. In the 10+ years of pymatgen, I recall the PT file only updated at most 4-5 times. This is not a set of data that is in a flux. Most of the values, e.g., atomic number, are never going to change. Most of the changeable values, e.g., melting point, electrical resistivity, etc., are not used in any part of the code. They are just there for some helper information and completeness. I fully expect someone who wants things like melting points not to rely on Pymatgen and to dig up literature sources for melting points. That only leaves things like atom/ion radii which may sometimes change and actually has an impact on some types of analysis.",
"> The current file can be updated with meta-data by adding a new dict key. The original PT data was generated from a reasonable source and certainly we can check the values from time to time. Even if we do it manually, what is really difficult about updating it from the current yaml?\n\nSure I'm happy to have a look at this side (also might give it a swing to separate the value and unit for clarity). The intention is more like to enrich our development side data source (the yaml) and drop things that is not needed during conversion to JSON for production to reduce RAM usage.\n\n> In the 10+ years of pymatgen, I recall the PT file only updated at most 4-5 times. \n\nThis might also be due to the fact that the single yaml file is so hard to read? For example I could get a table for \"Electrical resistivity\" from a handbook, but to actually validate the values, I would need to:\n- Either write a helpful script to aggregate this property for each element (I admit this is not hard though)\n- Do it the hard way: search for \"Electrical resistivity\" and jump between each element for like 100+ times\n\n> This is not a set of data that is in a flux. Most of the values, e.g., atomic number, are never going to change. \n\nYes these I agree.\n\n> Most of the values, e.g., atomic number, are never going to change. Most of the changeable values, e.g., melting point, electrical resistivity, etc., are not used in any part of the code. They are just there for some helper information and completeness.\n\nThis is the reason I assume we could selectively/lazily load only needed properties. But I agree it's easier to have a single JSON file for production.\n\n---\n\nApologize, I noticed a typo in the title, it should be \"one property per file\", i.e. one file for core and static property as you said (atomic mass, atomic number and so on), and other properties (e.g. \"Thermal conductivity\" and so on) in a single file and lazily load. Just to make sure I wasn't misleading the discussion\n\nAnd thanks for the helpful discussion in any case, I would give it a try and see how everything goes a bit later."
] | 2025-03-28T14:02:43
| 2025-04-01T10:39:36
|
2025-04-01T10:39:36Z
|
CONTRIBUTOR
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We might want to improve our development `periodic_table.yaml` recording:
- It's simply too large and monolithic as a single file, and as such maintaining it (and track git history) is quite hard.
- It doesn't carry enough metadata, for example we don't know the "source/reference" of each data entry, also some information/metadata is missing, for example the temperature at which "Electrical resistivity" is measured.
As such I want to propose that we split the recording to one property per file (perhaps core and static properties in one file), for the following benefits:
- Easier to maintain, we could add/modify certain property each time relatively easily, and also the git history would be much easier to read
- It's more similar to how certain data is presented in handbooks, for example the [CRC Handbook of Chemistry and Physics](https://en.wikipedia.org/wiki/CRC_Handbook_of_Chemistry_and_Physics 95th) have "Electrical Resistivity of Pure Metals" table which list the data for each element but at difference temperatures (and therefore we need more detailed metadata)
---
This is inspired by the following issues:
- https://github.com/materialsproject/pymatgen/issues/4312
- https://github.com/materialsproject/pymatgen/issues/4313
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I_kwDOACgets6wO9FV
| 4,337
|
Simple correction proposal for CifFile to handle properly CifBlock with same header
|
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[] | 2025-03-28T17:09:25
| 2025-03-28T17:13:41
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NONE
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### Feature Requested
As from 11/10/2023, the from_str() method of the CifFile class is annotated as:
"# TODO (@janosh, 2023-10-11) multiple CIF blocks with equal header will overwrite each other,"
"# latest taking precedence. maybe something to fix and test e.g. in test_cif_writer_write_file"
This class not supporting properly all possible CIF cases can be pretty annoying to build consistent code upon it, especially when it can be fixed pretty simply.
### Proposed Solution
I propose replacing the dict attribution line "dct[block.header] = block" at the end of the method by the following little trick to avoid key duplicates:
idx = 0
while f"{block.header}_pmg{idx}" in set(dct.keys()):
[indent]idx += 1
dct[f"{block.header}_pmg{idx}"] = block
If there is no duplication, the while loop is evaluated False and passed by so it does not change performance, and if there is duplicates it will only find the lowest integer index to apply to avoid duplicates.
After all, these dict keys are never put again inside the CIF data when eventually converting the CiFBlock back to a text file, as the actual header is still inside the CifBlock attributes.
If the dict key is needed anyway for getting back the header as-is, the consistent add of "pmg" before the actual number enable easier elimination and readability about where this index is coming from.
### Relevant Information
_No response_
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PR_kwDOACgets6Ql7rD
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|
[DEV] `ptable_table.json` generate with unified interface
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[
"I want to point out that ChatGPT can make this extremely easy. I passed ChatGPT the yaml and prompted it to \"separate out the units of each property for each element from the value, keeping the dictionary schema consistent\". Below is the result. \r\n\r\n```\r\nBelow is a Python script that reads your existing YAML file, scans every property for values that appear to include a numeric (or “no data”) part followed by units, and then rewrites those entries as dictionaries with separate “value” and “units” keys. The overall schema (i.e. the element keys and the structure for sub‐dictionaries, lists, etc.) remains unchanged.\r\n\r\nYou can add this file (for example, name it transform_periodic_table.py) to your project and run it. It will load periodic_table.yaml and produce a new file called periodic_table_transformed.yaml with the modified structure.\r\n\r\nBelow is the complete code:\r\n\r\nimport yaml\r\nimport re\r\n\r\ndef separate_units(val):\r\n \"\"\"\r\n If val is a string of the form \"<number> <unit>\" or \"no data <unit>\",\r\n separate out the numeric part (or \"no data\") and the unit.\r\n Otherwise, return the original value.\r\n \"\"\"\r\n if isinstance(val, str):\r\n # Match a number (integer or float) or the string \"no data\", followed by optional whitespace and the unit.\r\n m = re.match(r\"^(no data|\\d+(?:\\.\\d+)?)(?:\\s+(.*))?$\", val)\r\n if m:\r\n num_part = m.group(1)\r\n unit_part = m.group(2) or \"\"\r\n # Convert numeric part to float if possible and if not \"no data\"\r\n if num_part != \"no data\":\r\n try:\r\n num_val = float(num_part)\r\n except Exception:\r\n num_val = num_part\r\n else:\r\n num_val = num_part\r\n return {\"value\": num_val, \"units\": unit_part.strip()}\r\n return val\r\n\r\ndef transform(data):\r\n \"\"\"\r\n Recursively process the YAML data.\r\n If a value is a string, attempt to separate out a measurement and its unit.\r\n If the value is a dict or list, recursively process it.\r\n \"\"\"\r\n if isinstance(data, dict):\r\n new_data = {}\r\n for k, v in data.items():\r\n if isinstance(v, str):\r\n new_data[k] = separate_units(v)\r\n elif isinstance(v, (dict, list)):\r\n new_data[k] = transform(v)\r\n else:\r\n new_data[k] = v\r\n return new_data\r\n elif isinstance(data, list):\r\n return [transform(item) for item in data]\r\n else:\r\n return data\r\n\r\n# Load the original periodic table YAML file\r\nwith open(\"periodic_table.yaml\", \"r\", encoding=\"utf-8\") as f:\r\n data = yaml.safe_load(f)\r\n\r\n# Transform the data to separate units from the values\r\ntransformed_data = transform(data)\r\n\r\n# Write out the transformed data to a new YAML file\r\nwith open(\"periodic_table_transformed.yaml\", \"w\", encoding=\"utf-8\") as f:\r\n yaml.dump(transformed_data, f, sort_keys=False)\r\n\r\nprint(\"Transformation complete. New file saved as 'periodic_table_transformed.yaml'.\")\r\n\r\nHow It Works\r\n\t•\tseparate_units function:\r\nThis function uses a regular expression to detect a pattern where the value starts with either a number (integer or float) or the phrase \"no data\", followed by optional whitespace and then the unit. If the pattern is found, it returns a dictionary with the keys \"value\" and \"units\". If not, it simply returns the original value.\r\n\t•\ttransform function:\r\nThis function recursively walks through the entire YAML data structure. For each string value it finds, it applies the separate_units function. If the value is already a dictionary or a list, it recurses into that structure.\r\n\t•\tI/O:\r\nThe script reads your original periodic_table.yaml and writes the transformed version to periodic_table_transformed.yaml.\r\n\r\nOnce you run this script, your properties (for example, \"Boiling point: 3573 K\") will be converted into a nested structure like:\r\n\r\nBoiling point:\r\n value: 3573.0\r\n units: K\r\n\r\nThis keeps the overall dictionary schema (each element remains a top-level key with its properties) consistent.\r\n\r\nYou can now run this script in your PyCharm project, and the transformed file will be available for further use.\r\n```",
"Beautiful, thanks a ton for chiming in! That would be really helpful. I would work on this likely next week, enjoy the weekend :)",
"Quite weird, looks like the following elements are present in current (master branch) JSON, but not YAML (thought this JSON was generated from the YAML?):\r\n\r\n```\r\n{'Mc', 'Bh', 'Nh', 'Fl', 'Lv', 'Sg', 'Mt', 'Db', 'Ts', 'Og', 'Ds', 'Cn', 'Rf', 'Rg', 'Hs'}\r\n```",
"@DanielYang59 This looks like a lot of effort. Honestly, I think these features are barely going to be used. I suggest you simplify. Have a small json that contains only Z, atomic mass, electronegativity and ionic radii (which is always needed) and another that contains all other information and loaded with lru_cache. Most of the time, we only need to load the former.",
"> This looks like a lot of effort. Honestly, I think these features are barely going to be used. I suggest you simplify. Have a small json that contains only Z, atomic mass, electronegativity and ionic radii (which is always needed) and another that contains all other information and loaded with lru_cache. \r\n\r\nSure thing happy to do that ;) I think we're getting very close, just need a final clean up of the \"elemental data\" YAML source file.\r\n\r\nAs i noticed our current JSON is different from current YAML on several places:\r\n- YAML contains these element but JSON doesn't: `{'Mc', 'Bh', 'Nh', 'Fl', 'Lv', 'Sg', 'Mt', 'Db', 'Ts', 'Og', 'Ds', 'Cn', 'Rf', 'Rg', 'Hs'}`\r\n- JSON contains these properties but YAML doesn't: \"Ground level\", \"NMR Quadrupole Moment\"\r\n- The \"Electronic structure\" seems very different\r\n\r\n\r\nAfter this clean up, they should be consistent \r\n",
"@shyuep this PR is ready from my side, as it might be changing the core functionalities of pymatgen, I would like to push forward as slowly and carefully as possible, so I would separate the workflow into multiple steps:\r\n- This PR would only generate a duplicate JSON saved in `dev_scripts` without really changing our production recording\r\n- Any change/fix in production JSON recording would be submitted as a separate PR for visibility \r\n- More detailed TODOs see the description of this PR in `Follow up PRs`\r\n\r\n",
"This is merged since it only affects the dev directory. Thanks.",
"No problem at all, thanks for reviewing! \r\n\r\nYes I was trying to separate JSON generator and rewrite of JSON loader/parser for ptable.py into separate PRs."
] | 2025-03-28T17:35:55
| 2025-04-21T11:40:29
|
2025-04-20T19:45:37Z
|
CONTRIBUTOR
|
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## Summary
- Clean up development resources for `ptable_table.json` generation, #4336
- Allow sources/reference, #4313
- Remove seemingly unused `bvparm2011` (1d9ab4d276aaf8e46fca3c2925928f6fd12f5537) and `periodic_table.db`(benchmark from c80afb5d483ca3cc43ce0c155e7184eb8eb9e7b6)
### JSON/YAML generation directly from source with unified parser interface
```
Source recordings (different parsers for csv/yaml/...)
↓
Each generate one or more Property (property (hold unit/reference) -> element -> data)
↓
YAML for development and JSON for production
```
- Assume all values for the same property have the same unit
- Assume single reference for single property for now
---
### Follow up PRs
- [ ] JSON generator directly update `core.periodic_table.json`
- [ ] Have unit in a separate field, and need to update unit handle mechanism in `core.Element`
- [ ] Lazily load optional/extra properties
- [x] Some property are missing in current JSON (present in YAML): "Ground level", "NMR Quadrupole Moment"
- [x] "Electronic structure" in [current JSON](https://github.com/materialsproject/pymatgen/blob/master/src/pymatgen/core/periodic_table.json) and [YAML](https://github.com/materialsproject/pymatgen/blob/master/dev_scripts/periodic_table.yaml) seems quite different (new YAML for this PR uses the JSON to be consistent with production code)
- [ ] Resolve known differences in `_compare_json.py`
---
### Compare new JSON with current
Save a copy of current (master branch) JSON/YAML for quick lookup (uploading YAML not allowed): [master_json_yaml.zip](https://github.com/user-attachments/files/19547439/master_json_yaml.zip)
`pymatgen/dev_scripts/periodic_table_resources/_compare_json.py`
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I_kwDOACgets6wPtRD
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AseAtomsAdaptor -> Structure -> to(fmt="json") fails
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[] | 2025-03-28T18:39:33
| 2025-04-14T17:02:19
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2025-04-14T17:02:19Z
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### Python version
3.11.11
### Pymatgen version
2025.1.9
### Operating system version
_No response_
### Current behavior
When one uses Atoms -> AseAtomsAdaptor -> Structure -> to(fmt="json") the following error is raised:
`TypeError: Object of type bool_ is not JSON serializable`
### Expected Behavior
atoms = ...
adaptor = AseAtomsAdaptor()
structure = adaptor.get_structure(atoms)
string_structure = structure.to(fmt="json")
should work!
### Minimal example
I found the problem and a fix, this will cause the problem with any structure:
```Python
structure = ...
structure.lattice.pbc = tuple([np.bool_(a) for a in structure.lattice.pbc])
string_structure = structure.to(fmt="json")
```
This will fix it:
```Python
structure = ...
structure.lattice.pbc = tuple([bool(a) for a in structure.lattice.pbc])
string_structure = structure.to(fmt="json")
```
### Relevant files to reproduce this bug
_No response_
|
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# Issue Metrics
| Metric | Average | Median | 90th percentile |
| --- | --- | --- | ---: |
| Time to first response | 5 days, 20:06:06 | 2 days, 7:09:02 | 15 days, 4:49:57 |
| Time to close | 8 days, 3:53:51 | 6 days, 10:23:24 | 13 days, 23:12:57 |
| Time to answer | None | None | None |
| Metric | Count |
| --- | ---: |
| Number of items that remain open | 11 |
| Number of items closed | 5 |
| Number of most active mentors | 0 |
| Total number of items created | 16 |
| Title | URL | Author | Time to first response | Time to close | Time to answer |
| --- | --- | --- | --- | --- | --- |
| AseAtomsAdaptor -> Structure -> to(fmt="json") fails | https://github.com/materialsproject/pymatgen/issues/4339 | [bkmi](https://github.com/bkmi) | None | None | None |
| Simple correction proposal for CifFile to handle properly CifBlock with same header | https://github.com/materialsproject/pymatgen/issues/4337 | [E-Veillon](https://github.com/E-Veillon) | None | None | None |
| [Proposal] Better periodic table dev recording (`periodic_table.yaml`)- One property per file? | https://github.com/materialsproject/pymatgen/issues/4336 | [DanielYang59](https://github.com/DanielYang59) | 0:08:17 | None | None |
| Incorrect MAGMOM Handling in MPStaticSet When Using add_spin_by_site() | https://github.com/materialsproject/pymatgen/issues/4335 | [KylinGuo](https://github.com/KylinGuo) | None | None | None |
| Sort a molecular crystal supercell | https://github.com/materialsproject/pymatgen/issues/4333 | [pradipchm](https://github.com/pradipchm) | None | None | None |
| [RFC] Upstreaming methods for prototype assignment and protostructure assignment to pymatgen. | https://github.com/materialsproject/pymatgen/issues/4326 | [CompRhys](https://github.com/CompRhys) | None | 11 days, 7:25:10 | None |
| LATTICE_CONSTRAINTS tag not read properly for Incar | https://github.com/materialsproject/pymatgen/issues/4325 | [edansi](https://github.com/edansi) | 5 days, 21:38:56 | None | None |
| `Molecule.from_ase_atoms()` does not pass kwargs appropriately | https://github.com/materialsproject/pymatgen/issues/4320 | [Andrew-S-Rosen](https://github.com/Andrew-S-Rosen) | 8:11:04 | 6 days, 10:23:24 | None |
| Suspected little error in: pymatgen/transformations/advanced_transformations.py | https://github.com/materialsproject/pymatgen/issues/4318 | [jonasarnum](https://github.com/jonasarnum) | None | 15 days, 17:44:49 | None |
| Error when using CifParser to read 0.220_Mn2SiO4.mcif | https://github.com/materialsproject/pymatgen/issues/4316 | [Connor1y](https://github.com/Connor1y) | 0:30:20 | 3 days, 1:44:30 | None |
| Encoding issues in robocrystallographer output on the website | https://github.com/materialsproject/pymatgen/issues/4314 | [fxcoudert](https://github.com/fxcoudert) | 4 days, 6:07:00 | 4 days, 6:11:20 | None |
| More sources/references in periodic table | https://github.com/materialsproject/pymatgen/issues/4313 | [CakeIsYummy21](https://github.com/CakeIsYummy21) | None | None | None |
| Selen having a way too low resistance | https://github.com/materialsproject/pymatgen/issues/4312 | [CakeIsYummy21](https://github.com/CakeIsYummy21) | 24 days, 12:00:58 | None | None |
| Fix `SpaceGroup.symbol` to return short HM symbol always (incl. monoclinic spacegroups) | https://github.com/materialsproject/pymatgen/issues/4310 | [janosh](https://github.com/janosh) | None | None | None |
| `ImportError`: cannot import name `'SymmetryGroup'` from partially initialized module `'pymatgen.symmetry.groups'` | https://github.com/materialsproject/pymatgen/issues/4309 | [janosh](https://github.com/janosh) | None | None | None |
| Monthly issue metrics report | https://github.com/materialsproject/pymatgen/issues/4308 | [github-actions[bot]](https://github.com/github-actions[bot]) | None | None | None |
_This report was generated with the [Issue Metrics Action](https://github.com/github/issue-metrics)_
Search query used to find these items: `repo:materialsproject/pymatgen is:issue created:2025-03-01..2025-03-31 -reason:"not planned"`
|
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PR_kwDOACgets6Q6HIM
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Remove "Electrical resistivity" for Se as "high"
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[] | 2025-04-01T10:09:33
| 2025-04-01T15:41:41
|
2025-04-01T15:27:29Z
|
CONTRIBUTOR
|
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### Summary
- Remove "Electrical resistivity" for Se as "high", to fix #4312
Current the data for Electrical resistivity of Se is "high" (with `10<sup>-8</sup> Ω m` as the unit), and our parser would interpret it to:
```python
from pymatgen.core import Element
print(Element.Se.electrical_resistivity) # 1e-08 m ohm
```
This is the only data as "high" in `periodic_table.json` AFAIK.
After this, it would be None with a warning:
```
/Users/yang/developer/pymatgen/debug/test_elements.py:3: UserWarning: No data available for electrical_resistivity for Se
print(Element.Se.electrical_resistivity)
None
```
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PR_kwDOACgets6Q6RzD
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|
Correct Mn "ionic radii" in `core.periodic_table.json`
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[] | 2025-04-01T10:29:50
| 2025-04-01T15:41:35
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2025-04-01T15:28:30Z
|
CONTRIBUTOR
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## Summary
- Correct Mn "ionic radii" in `core.periodic_table.json`
Our csv parser should copy the high spin ionic radii to the base entry:
<img width="858" alt="image" src="https://github.com/user-attachments/assets/00f3ecff-3c78-4583-90b8-106ed362b78b" />
https://github.com/materialsproject/pymatgen/blob/4c7892f5c9dcc51a1389b3ad2ada77632989a13e/dev_scripts/update_pt_data.py#L84-L87
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| 2,963,026,988
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PR_kwDOACgets6Q6Usf
| 4,343
|
Drop duplicate `iupac_ordering` entries in `core.periodic_table.json`
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[] | 2025-04-01T10:35:38
| 2025-04-15T16:39:10
|
2025-04-15T16:37:07Z
|
CONTRIBUTOR
|
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## Summary
- Drop duplicate `iupac_ordering` entries in `core.periodic_table.json`
If I understand correctly, the `Element.iupac_ordering` should access the other "IUPAC ordering" instead:
https://github.com/materialsproject/pymatgen/blob/4c7892f5c9dcc51a1389b3ad2ada77632989a13e/src/pymatgen/core/periodic_table.py#L843-L850
AFAIK there's [no places across the code base accessing it](https://github.com/search?q=repo%3Amaterialsproject%2Fpymatgen+iupac_ordering+language%3APython&type=code&l=Python):
https://github.com/materialsproject/pymatgen/blob/55798119194076fd28cb28b539423779905c6ff9/tests/core/test_periodic_table.py#L327-L329
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| 2,963,220,053
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PR_kwDOACgets6Q6_eP
| 4,344
|
[Breaking] Update "electron affinities" in `periodic_table.json`
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[
"This one we need to be careful about. I am unclear where the old values come from. I believe it is the official Pauling definition. It might be old but it is what people have been using all this while. This can potentially cause havoc since MP uses the electronegativity in generating the formula.",
"Yes can never be too careful when changing such core data recordings, looks like to me it might came from the same source (wikipedia) as used in the PR (not sure if wikipedia updated its recording at some point?):\r\nhttps://github.com/materialsproject/pymatgen/blob/55798119194076fd28cb28b539423779905c6ff9/dev_scripts/update_pt_data.py#L242-L247",
"Maybe a quick check would be to query for all MP formulas, rerun the Compositiion parsing and reduced formula nad compare the results? If there are no changes to formulas, we can potentially consider it. But again, this is something often used. E.g., matminer uses it as a feature in some instances.",
"Just to make sure we're on the same page, this PR tries to update the \"electron affinity\" not the \"electronegativity\" (`X`):\r\n\r\nhttps://github.com/materialsproject/pymatgen/blob/55798119194076fd28cb28b539423779905c6ff9/src/pymatgen/core/periodic_table.py#L358-L361\r\n\r\nhttps://github.com/materialsproject/pymatgen/blob/55798119194076fd28cb28b539423779905c6ff9/src/pymatgen/core/periodic_table.py#L295-L307\r\n\r\nAnd indeed `Composition` uses `X` to sort the formula: \r\nhttps://github.com/materialsproject/pymatgen/blob/55798119194076fd28cb28b539423779905c6ff9/src/pymatgen/core/composition.py#L365-L373\r\n\r\nAnd AFAIK there's no places across pymatgen accessing this property: \r\n<img width=\"438\" alt=\"image\" src=\"https://github.com/user-attachments/assets/c0d0da8f-2fe6-4fad-8b5d-03c6bc3cffd9\" />\r\n\r\n\r\nAnd [I didn't see any place in matminer trying to use it either](https://github.com/search?q=repo%3Ahackingmaterials%2Fmatminer%20electron_affinity&type=code)\r\n",
"Ah my bad... I thought it was the electronegativity. Electron affinity is much less problematic since I don't think there are any critical use of it in normal operations.",
"No worries at all, never hurt to double check in any case :)"
] | 2025-04-01T11:56:51
| 2025-04-20T19:38:50
|
2025-04-20T19:36:56Z
|
CONTRIBUTOR
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## Summary
Looks like some [data entries from Wikipedia](https://en.wikipedia.org/wiki/Electron_affinity_(data_page)) changed quite dramatically (some are just flucations), not sure if we want to update them?
https://github.com/materialsproject/pymatgen/blob/4c7892f5c9dcc51a1389b3ad2ada77632989a13e/dev_scripts/update_pt_data.py#L243-L246
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| 2,963,397,275
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PR_kwDOACgets6Q7mTt
| 4,345
|
Fix inconsistent "Van der waals radius" and "Metallic radius" in `core.periodic_table.json`
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[] | null |
[
"I asked ChatGPT to do some validation. See below. Of course, ChatGPT notoriously can hallucinate. So you might want to just quickly check the discrepancies.\r\n\r\n```\r\nI have cross-referenced the van der Waals and metallic radii in your table with authoritative public sources, including Bondi’s 1964 compilation and data from WebElements and Wikipedia. Here’s a summary of the findings:\r\n\r\n⸻\r\n\r\n✅ Values Consistent with Public Sources\r\n\r\nThe following elements have van der Waals and/or metallic radii that align with widely accepted reference values:\r\n\t•\tHydrogen (H): VDW radius of 1.20 Å matches Bondi’s value.\r\n\t•\tCarbon (C): VDW radius of 1.70 Å is consistent with Bondi.\r\n\t•\tNitrogen (N): VDW radius of 1.55 Å aligns with Bondi.\r\n\t•\tOxygen (O): VDW radius of 1.52 Å matches Bondi.\r\n\t•\tFluorine (F): VDW radius of 1.47 Å is consistent with Bondi.\r\n\t•\tPhosphorus (P): VDW radius of 1.80 Å aligns with Bondi.\r\n\t•\tSulfur (S): VDW radius of 1.80 Å matches Bondi.\r\n\t•\tChlorine (Cl): VDW radius of 1.75 Å is consistent with Bondi.\r\n\t•\tCopper (Cu): VDW radius of 1.40 Å aligns with Bondi.\r\n\t•\tSodium (Na): VDW radius of 2.27 Å matches Bondi.\r\n\t•\tPotassium (K): VDW radius of 2.75 Å is consistent with Bondi.\r\n\t•\tCesium (Cs): VDW radius of 3.43 Å aligns with Bondi.\r\n\t•\tIodine (I): VDW radius of 1.98 Å matches Bondi.\r\n\t•\tXenon (Xe): VDW radius of 2.16 Å is consistent with Bondi.\r\n\t•\tMercury (Hg): VDW radius of 1.55 Å aligns with Bondi.\r\n\r\n⸻\r\n\r\n⚠️ Minor Discrepancies Noted\r\n\r\nSome elements exhibit slight differences between your table and public sources, which may be attributed to variations in measurement methods or coordination numbers:\r\n\t•\tLithium (Li): Your VDW radius is 1.82 Å; Alvarez (2014) reports 2.12 Å.\r\n\t•\tBeryllium (Be): Your VDW radius is 1.53 Å; Alvarez (2014) reports 1.98 Å.\r\n\t•\tNickel (Ni): Your VDW radius is 1.63 Å; Alvarez (2014) reports 2.40 Å.\r\n\t•\tSilver (Ag): Your VDW radius is 1.72 Å; Alvarez (2014) reports 2.53 Å.\r\n\t•\tGold (Au): Your VDW radius is 1.66 Å; Alvarez (2014) reports 2.32 Å.\r\n\t•\tLead (Pb): Your VDW radius is 2.02 Å; Alvarez (2014) reports 2.60 Å.\r\n\r\nThese discrepancies are within acceptable ranges, considering the differences in data sources and measurement techniques.\r\n\r\n⸻\r\n\r\nℹ️ Notes on Missing Data\r\n\t•\tMetallic Radii: For elements where “no data” is listed under metallic radius, it’s important to note that metallic radii are typically defined for metals in a solid state with specific coordination numbers. Nonmetals and noble gases do not have metallic radii in the conventional sense, hence the absence of data.\r\n\t•\tVan der Waals Radii: Similarly, for some transition metals and inner transition metals, van der Waals radii are less commonly reported due to the complexity of their electron configurations and bonding environments.\r\n\r\n⸻\r\n\r\nIf you require a detailed comparison for specific elements or further clarification on any discrepancies, feel free to ask!```",
"Hi @shyuep thanks for helping me double check. \r\n\r\nThis PR doesn't intend to validate the data source itself, but to ensure our `periodic_table.json` is consistent with its source (for vdw radii it's [`dev_scripts/radii.csv`](https://github.com/materialsproject/pymatgen/blob/master/dev_scripts/radii.csv)) (the `periodic_table.yaml` on the other hand, is the same as the CSV source in this regard)\r\n\r\nFor example the vdw radius of Ag is 1.72 in the source CSV and YAML, but is 2.11 in the JSON:\r\nhttps://github.com/materialsproject/pymatgen/blob/e301acde0ed572e6ede8616f3f200a314966e103/dev_scripts/radii.csv?plain=1#L48\r\nhttps://github.com/materialsproject/pymatgen/blob/e301acde0ed572e6ede8616f3f200a314966e103/src/pymatgen/core/periodic_table.json#L301\r\nhttps://github.com/materialsproject/pymatgen/blob/e301acde0ed572e6ede8616f3f200a314966e103/dev_scripts/periodic_table.yaml#L111\r\n\r\n---\r\n\r\nWith https://github.com/materialsproject/pymatgen/pull/4338, we could then validate the radii or whatever other property, as the final JSON generation would have a more explainable/reliable pipeline."
] | 2025-04-01T13:07:15
| 2025-04-17T17:14:17
|
2025-04-17T16:04:34Z
|
CONTRIBUTOR
|
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## Summary
- Fix inconsistent "Van der waals radius" and "Metallic radius" in `core.periodic_table.json`
Currently some of the radii from JSON is different from our CSV (the YAML is the same):
After this PR:
```
Element | Van der waals radius | Metallic radius
------------------------------------------------------
H | 1.2 | no data
He | 1.4 | no data
Li | 1.82 | 1.52
Be | 1.53 | 1.12
B | 1.92 | no data
C | 1.7 | no data
N | 1.55 | no data
O | 1.52 | no data
F | 1.47 | no data
Ne | 1.54 | no data
Na | 2.27 | 1.86
Mg | 1.73 | 1.6
Al | 1.84 | 1.43
Si | 2.1 | no data
P | 1.8 | no data
S | 1.8 | no data
Cl | 1.75 | no data
Ar | 1.88 | no data
K | 2.75 | 2.27
Ca | 2.31 | 1.97
Sc | 2.11 | 1.62
Ti | 2.11 | 1.47
V | 2.07 | 1.34
Cr | 2.06 | 1.28
Mn | 2.05 | 1.27
Fe | 2.04 | 1.26
Co | 2 | 1.25
Ni | 1.63 | 1.24
Cu | 1.4 | 1.28
Zn | 1.39 | 1.34
Ga | 1.87 | 1.35
Ge | 2.11 | 1.39
As | 1.85 | no data
Se | 1.9 | no data
Br | 1.85 | 1.14
Kr | 2.02 | no data
Rb | 3.03 | 2.48
Sr | 2.49 | 2.15
Y | 2.32 | 1.8
Zr | 2.23 | 1.6
Nb | 2.18 | 1.46
Mo | 2.17 | 1.39
Tc | 2.16 | 1.36
Ru | 2.13 | 1.34
Rh | 2.1 | 1.34
Pd | 1.63 | 1.37
Ag | 1.72 | 1.44
Cd | 1.58 | 1.51
In | 1.93 | 1.67
Sn | 2.17 | 1.58
Sb | 2.06 | 1.61
Te | 2.06 | no data
I | 1.98 | no data
Xe | 2.16 | no data
Cs | 3.43 | 2.65
Ba | 2.68 | 2.22
La | 2.43 | 1.87
Ce | 2.42 | 1.818
Pr | 2.4 | 1.824
Nd | 2.39 | 1.814
Pm | 2.38 | 1.834
Sm | 2.36 | 1.804
Eu | 2.35 | 1.804
Gd | 2.34 | 1.804
Tb | 2.33 | 1.773
Dy | 2.31 | 1.781
Ho | 2.3 | 1.762
Er | 2.29 | 1.761
Tm | 2.27 | 1.759
Yb | 2.26 | 1.76
Lu | 2.24 | 1.738
Hf | 2.23 | 1.59
Ta | 2.22 | 1.46
W | 2.18 | 1.39
Re | 2.16 | 1.37
Os | 2.16 | 1.35
Ir | 2.13 | 1.355
Pt | 1.75 | 1.385
Au | 1.66 | 1.44
Hg | 1.55 | 1.51
Tl | 1.96 | 1.7
Pb | 2.02 | 1.75
Bi | 2.07 | 1.82
Po | 1.97 | 1.53
At | 2.02 | no data
Rn | 2.2 | no data
Fr | 3.48 | no data
Ra | 2.83 | 2.293
Ac | 2.47 | 1.878
Th | 2.45 | 1.79
Pa | 2.1 | 1.63
U | 1.86 | 1.56
Np | 2.39 | 1.55
Pu | 2.43 | 1.59
Am | 2.44 | 1.73
Cm | 2.45 | 1.74
Bk | 2.44 | 1.7
Cf | 2.45 | 1.86
Es | 2.45 | 1.86
Fm | 2.45 | no data
Md | 2.46 | no data
No | 2.46 | no data
```
Script:
```python
import json
from pathlib import Path
from pymatgen.core import Element
def print_radius_table(json_file: str | Path) -> None:
with open(json_file, encoding="utf-8") as f:
data = json.load(f)
headers = ["Element", "Van der waals radius", "Metallic radius"]
print(f"{headers[0]:<8} | {headers[1]:<22} | {headers[2]:<16}")
print("-" * 54)
for elem in Element:
elem = str(elem)
vdw = data[elem].get("Van der waals radius", "")
metal = data[elem].get("Metallic radius", "")
print(f"{elem:<8} | {str(vdw):<22} | {str(metal):<16}")
if __name__ == "__main__":
print_radius_table("periodic_table.json")
```
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I_kwDOACgets6w2msL
| 4,346
|
WAVECAR read error: "ValueError: Incorrect vasp_type=None"
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[
"Can you first upgrade your version of pymatgen by running `pip install --upgrade pymatgen`? A version from 2023 is likely missing updates to VASP parsing functionality"
] | 2025-04-02T17:37:44
| 2025-04-14T15:55:33
|
NONE
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### Python version
Python 3.11.10
### Pymatgen version
2023.10.11
### Operating system version
Ubuntu 22.04
### Current behavior
My WAVECAR file for a PBEsol+U calculation is not being read.
I have also tried setting vasp_type = 'std' but it still gives me an error.
### Expected Behavior
The other PBEsol+U calculations performed with the same tags were read without running into this error.
### Minimal example
```Python
```
### Relevant files to reproduce this bug
I have added the IBZKPT, KPOINTS, INCAR, and CONTCAR files to this [zip file](https://github.com/user-attachments/files/19572981/relevant_files.zip).
|
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| 2,967,224,240
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PR_kwDOACgets6RImT7
| 4,347
|
Clean up optional Ubuntu CI pipeline, skip `BoltzTraP2 ` install in pipeline for now
|
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[] | 2025-04-02T18:30:34
| 2025-04-14T17:38:14
|
2025-04-14T17:02:36Z
|
CONTRIBUTOR
|
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- ~~Skip `BoltzTraP2` in pipeline for now, now sure why it's failing (build works on my Ubuntu 22.04/24.04 local machine) but I would open an issue on their side~~
- Merge `ZEO++` install into optional Ubuntu pipeline (also add macos to this optional, never hurt to get more test coverage?)
- [ ] Waiting for response from `BoltzTraP2` issue/MR, otherwise its install might start to fail again at some point owing to the cmake deprecation
I guess it's safer to just comment out bolztrap2 for now until the MR is merged, otherwise it would still start to fail if cmake is bumped in github runners
---
Update: 7 Apr, for some reason `BoltzTraP2` is passing on CI now, but I could indeed recreate the issue locally:
- https://gitlab.com/sousaw/BoltzTraP2/-/issues/7
Update 2: looks like is a cmake deprecation (not sure what version of cmake is used in CI runners though): https://gitlab.com/sousaw/BoltzTraP2/-/merge_requests/28
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PR_kwDOACgets6RJmhc
| 4,348
|
Remove deprecated grain boundary analysis
|
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[] | 2025-04-02T20:50:08
| 2025-04-15T16:18:01
|
2025-04-15T16:11:04Z
|
CONTRIBUTOR
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- Remove deprecated grain boundary analysis https://github.com/materialsproject/pymatgen/blob/55798119194076fd28cb28b539423779905c6ff9/src/pymatgen/analysis/gb/grain.py#L7-L12
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I_kwDOACgets6xH_ue
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ICOHPLIST Parser in pymatgen Does Not Handle Missing Trailing Newline (Ref: lobsterpy#389)
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[] | 2025-04-04T08:34:26
| 2025-04-04T08:34:26
|
CONTRIBUTOR
|
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### Python version
Python 3.12.7
### Pymatgen version
2025.3.10
### Operating system version
Ubuntu 24.04.2 LTS
### Current behavior
When using LOBSTER version 5.1.1, the generated ICOHPLIST.lobster file may not include a trailing newline. This causes the Icohplist parser in pymatgen’s outputs module to miscount the number of bond entries. For example, ICOHPLIST.lobster is parsed with 1079 entries, while COHPCAR.lobster contains 1080 bonds. This mismatch triggers a ValueError:
`ValueError: COHPCAR and ICOHPLIST do not fit together`
### Expected Behavior
The parser should correctly count the bond entries even if ICOHPLIST.lobster lacks a trailing newline. For instance, by using Python’s splitlines() method (which automatically handles missing trailing newlines) and filtering out any empty lines, the bond count should accurately reflect the intended number of bonds, allowing the analysis to proceed without error.
### Minimal example
```Python
```
### Relevant files to reproduce this bug
**Steps to reproduce the behavior:**
1. Generate LOBSTER output files (ICOHPLIST.lobster and COHPCAR.lobster) using LOBSTER version 5.1.1.
1. Verify that ICOHPLIST.lobster does not end with a trailing newline (e.g., by opening the file in a text editor or running:
```
wc -l ICOHPLIST.lobster
grep -n "." ICOHPLIST.lobster
```
to compare the line counts).
1. Run a pymatgen-based workflow (or a LobsterPy/pymatgen script) that reads these files.
1. The parser miscounts the bonds and raises the ValueError.
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PR_kwDOACgets6RXxaw
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fix(outputs.py): handle missing trailing newline in ICOHPLIST.lobster
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[
"Thanks, @alibh95, for raising this fix. Can you please also adapt the failing test in the PR?",
"Please provide a unittest to check for this issue. Thanks.",
"Hi @alibh95 , it would be great if you can fix the newly added test that is failing and also adapt the existing failing test to reflect correct number of bonds",
"@shyuep I have added the unittest for this issue.\r\n@naik-aakash The failing tests are corrected in this commit.",
"Thanks!"
] | 2025-04-04T08:46:28
| 2025-05-01T21:17:12
|
2025-05-01T21:17:06Z
|
CONTRIBUTOR
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LOBSTER 5.1.1 sometimes produces an ICOHPLIST.lobster file without a trailing newline, which causes the Icohplist parser to miscount bond entries (e.g., 1079 vs. 1080). This commit updates the file-reading logic in Icohplist.__init__ to use splitlines() and filters out any blank lines, ensuring that only valid, non-empty lines are parsed. This change prevents the ValueError ("COHPCAR and ICOHPLIST do not fit together") and aligns the bond count with COHPCAR.lobster.
Ref: #4349 and JaGeo/lobsterpy#389
## Summary
Major changes:
- Update file reading in Icohplist.__init__ to use splitlines() instead of split("\n").
- Filter out blank lines from the ICOHPLIST.lobster file to ensure accurate bond count.
## Checklist
- [ ] Google format doc strings added. Check with `ruff`.
- [ ] Type annotations included. Check with `mypy`.
- [ ] Tests added for new features/fixes.
- [ ] If applicable, new classes/functions/modules have [`duecredit`](https://github.com/duecredit/duecredit) `@due.dcite` decorators to reference relevant papers by DOI ([example](https://github.com/materialsproject/pymatgen/blob/91dbe6ee9ed01d781a9388bf147648e20c6d58e0/pymatgen/core/lattice.py#L1168-L1172))
Tip: Install `pre-commit` hooks to auto-check types and linting before every commit:
```sh
pip install -U pre-commit
pre-commit install
```
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Consistent serialization method naming (`as_dict` preferred over `to_dict`?)
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[
"Please contact the jdftx PR submitter on this. Since htis is relatively new, we can potentially just modify it without keeping backwards compaitbility. But it is trivial to add a to_dict = as_dict to maintain backwards compatibility anyway,.\n",
"Thanks for the input, I could do that (just to confirm I wasn't getting anything wrong)",
"related to this in composition we have \n```python\n @property\n def to_reduced_dict(self) -> dict[str, float]:\n \"\"\"\n Returns:\n dict[str, float]: element symbols mapped to reduced amount e.g. {\"Fe\": 2.0, \"O\":3.0}.\n \"\"\"\n return self.reduced_composition.as_dict()\n\n @property\n def to_weight_dict(self) -> dict[str, float]:\n \"\"\"\n Returns:\n dict[str, float]: weight fractions of each component, e.g. {\"Ti\": 0.90, \"V\": 0.06, \"Al\": 0.04}.\n \"\"\"\n return {str(el): self.get_wt_fraction(el) for el in self.elements}\n\n @property\n def to_data_dict(\n self,\n ) -> dict[\n Literal[\"reduced_cell_composition\", \"unit_cell_composition\", \"reduced_cell_formula\", \"elements\", \"nelements\"],\n Any,\n ]:\n \"\"\"\n Returns:\n dict with the following keys:\n - reduced_cell_composition\n - unit_cell_composition\n - reduced_cell_formula\n - elements\n - nelements.\n \"\"\"\n return {\n \"reduced_cell_composition\": self.reduced_composition,\n \"unit_cell_composition\": self.as_dict(),\n \"reduced_cell_formula\": self.reduced_formula,\n \"elements\": list(map(str, self)),\n \"nelements\": len(self),\n }\n```\n\nI've been wondering about whether they should either be `Composition.as_xyz_dict()` methods or `Composition.xyz_dict` properties. When calling `Composition.to_weight_dict` I almost always add the parentheses because \"to\" makes it feel like a method name. Very much a nit but related to the consistency of \"to\" vs. \"as\" for serialization. ",
"Let's be consistent in a backwards compatible manner and deprecate the necessary methods. ",
"Thanks for the input, would do!\n\nAlso noticed many methods named as `to_xxx` (not necessarily serialization to dict, but general \"exporting\" like `to_str`, `to_csv`, and so on), do we want to enforce consistent `as_xxx` naming for everything \"exporting\"? \n\n---\n\nAlso noticed it's `to_json` under `MSONable`: https://github.com/materialsvirtuallab/monty/blob/c0f06ed082ef4defec6a82450256f431a6903ec0/src/monty/json.py#L235-L239\n\nBut there's nothing we could do able `to` because `as` is a builtin keyword:\nhttps://github.com/materialsproject/pymatgen/blob/5df11d9acb031741a5cc5f03b56b76de21a1ed23/src/pymatgen/core/structure.py#L521-L525"
] | 2025-04-04T10:49:53
| 2025-04-10T15:44:29
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2025-04-10T15:44:28Z
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CONTRIBUTOR
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Not sure but I guess `as_dict` is the preferred name in pymatgen and [MSONable](https://github.com/materialsvirtuallab/monty/blob/7eb2b29c397095cbb5e8f63d1766e3c92db8c8c1/src/monty/json.py#L154)?
<img width="298" alt="Image" src="https://github.com/user-attachments/assets/b1978148-ce6b-4a83-8ae3-07a040202c06" />
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Replace `to_dict` with `as_dict`
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[
"Only as dict. Pls leave the rest as is. There is no good reason to break everything for this. ",
"> Only as dict. Pls leave the rest as is. There is no good reason to break everything for this.\r\n\r\nOkay sure",
"BTW, I noticed there're two `contributing.md`? https://github.com/materialsproject/pymatgen/blob/master/CONTRIBUTING.md and https://github.com/materialsproject/pymatgen/blob/master/docs/contributing.md"
] | 2025-04-04T15:54:31
| 2025-04-10T15:48:26
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2025-04-10T15:44:27Z
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CONTRIBUTOR
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- Deprecate `to_dict` with `as_dict`, to close #4351
- Updated `contributing.md` to note preferred naming convention
- [x] Regenerate `docs`
The recent additional of JDFTx IOs have a relatively short grace period of 6 months, and others have one year
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| 4,354
|
The constraints changed after switch back and forth from ase atoms
|
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[
"It was indeed my commit that broke things. Thanks for reporting!\nYour example should be working with my new commit."
] | 2025-04-09T19:34:01
| 2025-04-10T17:59:42
|
2025-04-10T17:59:42Z
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NONE
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### Python version
3.11.5
### Pymatgen version
2025.3.10
### Operating system version
Ubuntu 22.04 LTS
### Current behavior
Converting the structure back and forth between ase atoms and structure, the constraints will change in the latest versions. (2025.3.10), but it works correct with the old version (2024.11.13).
Not sure if this relates the this new feature. (AseAtomsAdaptor: Support arbitrary selective dynamics constraints @yantar92 . )
results for 2025.3.10
```
cnt1.constraints: [FixAtoms(indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])]
cnt2.constraints: [FixAtoms(indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])]
```
### Expected Behavior
results for 2024.11.13
```
cnt1.constraints: [FixAtoms(indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])]
cnt2.constraints: [FixAtoms(indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])]
```
### Minimal example
```Python
from pymatgen.core import Structure
import ase
from ase.build import nanotube
import numpy as np
from ase.constraints import FixAtoms
cnt1 = nanotube(6, 0, length=1)
cnt1.set_pbc(True)
cnt1.set_cell([20.0, 20.0, cnt1.get_cell()[2,2]])
constraint_atoms = FixAtoms(mask=[True]*12+[False]*12)
cnt1.set_constraint(constraint_atoms)
molecule = Structure.from_ase_atoms(cnt1)
cnt2 = molecule.to_ase_atoms()
print("cnt1.constraints:",cnt1.constraints)
print("cnt2.constraints:",cnt2.constraints)
```
### Relevant files to reproduce this bug
_No response_
|
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PR_kwDOACgets6SIba7
| 4,355
|
Fix round-trip constraints handling of `AseAtomsAdaptor`
|
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[
"> In your comment, can you please link to the prior PR where this bug was introduced?\r\n\r\nThe original PR is #4229. (I am not 100% sure which comment you are referring to: here, in the bug report, or in the commit message).",
"Thanks, @yantar92! That's the one. I wanted to make sure it was linked to this issue PR since it wasn't mentioned in #4354.",
"Many thanks for the fix."
] | 2025-04-10T16:35:29
| 2025-04-10T17:59:41
|
2025-04-10T17:59:41Z
|
CONTRIBUTOR
|
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* src/pymatgen/io/ase.py (AseAtomsAdaptor.get_structure): When no explicit constraint is given for a site in ASE Atoms object, use "T T T" selective dynamics (no constraint). The old code is plain wrong.
* tests/io/test_ase.py (test_back_forth): Add new test case.
Fixes #4354.
Thanks to @yaoyi92 for reporting!
|
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PR_kwDOACgets6SJ0RY
| 4,356
|
`core.Lattice` cast `pbc` to `bool` (possibly `np.bool_`)
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2025-04-14T17:02:18Z
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CONTRIBUTOR
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### Summary
- `core.Lattice` cast `pbc` to `bool` (possibly `np.bool_`), to close #4339
|
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PR_kwDOACgets6SJ_af
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Fix circular import of `SymmOp`
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[
"No that was an import I used for debugging and forgot to delete, apologies for leaving it in there.",
"No worries at all, it simply means we're missing a test for this :D"
] | 2025-04-10T19:46:12
| 2025-04-10T20:16:03
|
2025-04-10T20:15:30Z
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CONTRIBUTOR
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- Fix circular import of `SymmOp`, to close #4309
Looks like the import of `SymmOp` was added in #4177, @tpurcell90 do we have a reason to import `SymmOp` at runtime? https://github.com/materialsproject/pymatgen/blob/482ffd01312e7607c58bdd3ed285bf8a45e405bf/src/pymatgen/symmetry/groups.py#L24-L33
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Make `pybtex` optional
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[] | 2025-04-10T21:24:02
| 2025-04-10T22:13:08
|
2025-04-10T22:13:01Z
|
CONTRIBUTOR
|
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- Make `pybtex` optional, to close #4285
Rationale (see details in #4285):
- It's only used in two places across the code base: `cif.CifParser.get_bibtex_string` and `util.provenance.StructureNL`
- `pybtex` hasn't got any release since Jan 2021, and doesn't support Python 3.13 yet
I'm not entirely sure if this is a frequently used function in pmg, if so I'm happy to look into alternatives to enable Python 3.13 support for pmg
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PR_kwDOACgets6SshXc
| 4,359
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Avoid repeated `Incar.get()` calls which slow down vasprun parsing by ~20-50%
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[
"Thanks!"
] | 2025-04-15T16:00:35
| 2025-04-15T16:10:48
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2025-04-15T16:10:41Z
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CONTRIBUTOR
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From profiling my `Vasprun` initialisations, I found that one of the main contributions to runtime was repeated `Incar.get()` calls:
https://github.com/materialsproject/pymatgen/blob/31aa2148f85fcd3f01461e269a06b0186f82a22b/src/pymatgen/io/vasp/inputs.py#L847
This can be slow when called many times because of the `UserDict.get()` implementation from `collections.abc` and formatting code. Because `incar.get("ML_LMFF")` was being used in the `Vasprun` parsing loop, this method was called e.g. 1e4 - 1e6+ times in my vasprun parsing. Changing this so that `incar.get("ML_LMFF")` is only called once to determine if the vasp run is an ML run, means `incar.get` only gets called ~20 times and reduces the vasprun parsing time by ~20-50% (depending on choices of `parse_dos` and `parse_projected_eigen`).
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PR_kwDOACgets6Ss3zA
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Speed up `Vasprun` parsing some more
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[
"Thanks. but I don't think we need to use string concat? I believe np.loadtxt would be able to handle the text without concat.",
"Example:\r\n```\r\nIn [1]: import numpy as np\r\n\r\nIn [3]: np.loadtxt([\"1 2\", \"3 4\"])\r\nOut[3]:\r\narray([[1., 2.],\r\n [3., 4.]])\r\n```",
"Even better, no reshaping needed.",
"Ah yes! Good points. Done ⬆️ "
] | 2025-04-15T16:40:35
| 2025-04-15T21:25:19
|
2025-04-15T21:25:19Z
|
CONTRIBUTOR
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Me again.
From further profiling and playing around, I found I could speed up `_parse_vasp_array` (one of the main bottlenecks when using `parse_dos = True` (default), `parse_eigen = True` (default) and/or `parse_projected_eigen = True` (False by default)), using `numpy`'s parse from string function.
e.g. parsing a SOC defect supercell vasprun via [`doped`](https://doped.readthedocs.io/en/latest/) with these updates (with `parse_projected_eigen=True` to get eigenvalues/magnetisation) decreases parsing time from ~8.5s to ~4.8s.
All changes here should be covered by tests already in the codebase.
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Replace `pybtex` with `bibtexparser`
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[
"I cannot say I'm a bibtex expert, at least all tests seem to pass (both for `cif` and `StructureNL`), anyone experienced with bibtex please free feel to review and comment :D"
] | 2025-04-15T18:07:51
| 2025-04-18T06:07:58
|
2025-04-18T02:59:39Z
|
CONTRIBUTOR
|
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## Summary
- Replace `pybtex` with `bibtexparser`
- [x] Add `bibtexparser` lower bound
- [x] Update requirements.txt (or any other lock file)
|
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fix(MVLSlabSet): convert DIPOL vector to pure Python list before writing INCAR
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[
"\nAdd a unit test pls. "
] | 2025-04-17T17:52:20
| 2025-04-18T02:59:07
|
2025-04-18T02:59:07Z
|
CONTRIBUTOR
|
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## Summary
When `auto_dipole=True`, `MVLSlabSet` was passing a NumPy array into the INCAR’s DIPOL tag, causing pymatgen to serialize it with brackets (e.g. `DIPOL = [0.1 0.2 0.3]`). VASP expects three space‑separated floats without any brackets: https://www.vasp.at/wiki/index.php/DIPOL.
This PR converts the computed center‑of‑mass array into a plain Python `list[float]` (via `.tolist()`), ensuring the INCAR writes
`DIPOL = 0.1 0.2 0.3`
instead of
`DIPOL = [0.1 0.2 0.3]`
which errors out with the recent VASP 6.4.
Major changes:
- fix 1: In `MVLSlabSet.incar_updates`, replace `center_of_mass` (a NumPy array) with a pure python list = `com.tolist()`
## Checklist
- [x] Google format doc strings added. Check with `ruff`.
- [x] Type annotations included. Check with `mypy`.
- [x] Tests added for new features/fixes.
- [x] If applicable, new classes/functions/modules have [`duecredit`](https://github.com/duecredit/duecredit) `@due.dcite` decorators to reference relevant papers by DOI ([example](https://github.com/materialsproject/pymatgen/blob/91dbe6ee9ed01d781a9388bf147648e20c6d58e0/pymatgen/core/lattice.py#L1168-L1172))
Tip: Install `pre-commit` hooks to auto-check types and linting before every commit:
```sh
pip install -U pre-commit
pre-commit install
```
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PR_kwDOACgets6TCmWt
| 4,363
|
Ensure `actual_kpoints_weights` is `list[float]` and add test
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[] | 2025-04-17T21:02:54
| 2025-04-17T21:35:49
|
2025-04-17T21:35:49Z
|
CONTRIBUTOR
|
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Just a minor fix; after #4360 the format of `actual_kpoints_weights` was changed from `list[float]` to `list[np.ndarray[float]]` (where these were `np.ndarray[float]` with a single value). Minor thing but this changes the format back to what it was before, and adds a quick test for this.
|
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I_kwDOACgets6zB_Fd
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|
The strange display style of bar (-) on next-gen.materialsproject.org.
|
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[] | null |
[
"This is not a pymatgen issue. Pls report to Materials Project.",
"I have reposted at https://matsci.org/t/the-strange-display-style-of-bar-on-next-gen-materialsproject-org/63076."
] | 2025-04-17T23:57:51
| 2025-04-18T13:24:27
|
2025-04-18T02:32:39Z
|
CONTRIBUTOR
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Hi there,
See below as shown [here](https://next-gen.materialsproject.org/materials?formula=TiN):
As you can see, the bar (-) on 3 is displayed with a bizarre and blurred style.
Regards,
Zhao
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PR_kwDOACgets6TF4rS
| 4,365
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Python 3.13 support
|
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[
"I would mark this PR as ready, but noticed [NP1 seems to fail on Python 3.13 on Windows](https://github.com/materialsproject/pymatgen/actions/runs/14532459539/job/40774628811) but I cannot recreate this locally. I would add a follow up PR/issue if I could find a recreation."
] | 2025-04-18T08:28:35
| 2025-04-20T20:31:40
|
2025-04-20T19:35:32Z
|
CONTRIBUTOR
|
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### Summary
- Python 3.13 support (previously #4100), to close #4284
- [ ] fix vampire install: https://github.com/richard-evans/vampire/issues/122
```
wget https://vampire.york.ac.uk/resources/release-5/vampire-5.0-linux.tar.gz
--2025-04-18 10:45:13-- https://vampire.york.ac.uk/resources/release-5/vampire-5.0-linux.tar.gz
Resolving vampire.york.ac.uk (vampire.york.ac.uk)... 144.32.124.80, 2001:630:61:17c::1:50
Connecting to vampire.york.ac.uk (vampire.york.ac.uk)|144.32.124.80|:443... connected.
ERROR: cannot verify vampire.york.ac.uk's certificate, issued by ‘CN=Sectigo ECC Organization Validation Secure Server CA,O=Sectigo Limited,L=Salford,ST=Greater Manchester,C=GB’:
Unable to locally verify the issuer's authority.
To connect to vampire.york.ac.uk insecurely, use `--no-check-certificate'
```
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I_kwDOACgets6zKX0g
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`MPRester` Issues
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[] | null |
[
"Also just to check, should developers of downstream packages consider the dropping of `mp-api` and new `MPRester` semantics/formats to be stable from now on, or are other breaking changes likely in the near future?",
"I will address these changes in a day or two. Feel free to report any further changes. Note that in most cases, I am aware of only issues that causes tests to break. If it is not tested, it means it is not an official functionality for the most part. But I will address any reasonable requests. \n\nAs for mp-api, it will still be around and maintained by the MP staff. But I prefer pymatgen to have its own high level interface. Philosophically, I prefer a more rapid, iterative pace of development and consistency of documentation in one location (rather than having inconsistent terms in different python and API documentation). You can always install mp-api and it will still work. Pymatgen just won't support it as an option to make sure that everything is consistent. ",
"This should be all done with the latest commits."
] | 2025-04-18T22:06:47
| 2025-04-19T02:04:03
|
2025-04-19T02:04:02Z
|
CONTRIBUTOR
|
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### Python version
Python 3.12
### Pymatgen version
2025.4.17
### Operating system version
MacOS
### Current behavior
There seems to be some issues with the updated `MPRester` behavior, after the dropping of `mp-api`.
- `property_data` does not appear to be used in `get_entries`, but is in the docstring. It would be good if we could use this like before to include properties like `band_gap`, `icsd_id` etc
https://github.com/materialsproject/pymatgen/blob/8794ffa5469a0eba95bb2ec7b96f778c6ff2a6fd/src/pymatgen/ext/matproj.py#L328-L383
- `get_entries_in_chemsys` says it allows the conventional `"Li-Ni-O"` type input, but the code actually assumes the input is a list of elements `["Li", "Ni", "O"]`
https://github.com/materialsproject/pymatgen/blob/8794ffa5469a0eba95bb2ec7b96f778c6ff2a6fd/src/pymatgen/ext/matproj.py#L399-L421
### Expected Behavior
Same behavior / functionality as before `mp-api` was dropped.
### Minimal example
```Python
```
### Relevant files to reproduce this bug
_No response_
|
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| 3,006,235,173
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PR_kwDOACgets6TMIh0
| 4,367
|
fix perturb bug that displaced all atoms equally
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[
"Thanks for fixing this. "
] | 2025-04-19T06:23:31
| 2025-04-20T03:13:45
|
2025-04-19T14:24:42Z
|
CONTRIBUTOR
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## Summary
Major changes:
- fix 1: If I'm not mistaken, #4270 added a random seed parameter to Structure.perturb() but ended up setting the same random vector for all atoms. This should probably be fixed by pulling the seed-setting line out of the get_rand_vec() function.
## Checklist
- [x] Google format doc strings added. Check with `ruff`.
- [x] Type annotations included. Check with `mypy`.
- [ ] Tests added for new features/fixes.
- [ ] If applicable, new classes/functions/modules have [`duecredit`](https://github.com/duecredit/duecredit) `@due.dcite` decorators to reference relevant papers by DOI ([example](https://github.com/materialsproject/pymatgen/blob/91dbe6ee9ed01d781a9388bf147648e20c6d58e0/pymatgen/core/lattice.py#L1168-L1172))
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PR_kwDOACgets6TNbHs
| 4,368
|
[WIP] Better input set generator and sets for io.lammps
|
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[] | 2025-04-19T21:06:52
| 2025-04-29T20:49:59
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NONE
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## Summary
This PR is part of an effort to include workflows for running LAMMPS via atomate2. A concurrent PR will be opened in atomate2 that has the flows, while this PR aims to update the input set generators to match the inputs required there.
These changes are based on initial work here: https://github.com/Matgenix/atomate2-lammps done by @ml-evs and @gbrunin. Also tagging in @esoteric-ephemera who helped structure the code in this PR, and @davidwaroquiers.
Major changes:
- Updated templates for running NVT/NPT/Minimization jobs with the upcoming flows in atomate2.
- Added in a data class to validate commonly utilized settings (based on the updated templates)
- The new input set generator is written (to the best of my ability) to provide input settings consistent with classical MD run through other codes on solids (matched defaults to ASE MD)
Marked this as a WIP as there might be further updates here dependent on the updates made in the concurrent atomate2 PR.
TODO:
- Add tests
- I've made most of the design decisions here assuming these sets will be used with solids through the Structure object, but one could also argue including support for Molecules. I'm looking for suggestions on how that can be incorporated without varying too much of the logic (ex : for the default inputs, atom styles, force field parameters)
- I'm also looking for suggestions on how to best check/validate user specified force field parameters, which is difficult to do considering the numerous force field formats that exist out there. The existing ForceField object in io.lammps.data is not very intuitive to set-up for a user and does not support lammps integrated MLIPs yet.
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I_kwDOACgets6zPnkM
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Wheel build in Py3.13 does not work
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[
"@DanielYang59 \n",
"Thanks for pinging me, I guess it has been fixed by 212af7f85b622c93dbc4db36a9586698cc4a8433? \n\n>[v2.21.2](https://github.com/pypa/cibuildwheel/releases/tag/v2.21.2)\n> ✨ Adds official support for running on Python 3.13 (https://github.com/pypa/cibuildwheel/pull/2026)",
"Let me try running the release flow again.",
"Thanks. BTW I don't have access to trigger any workflow so cannot really help here, sorry",
"~~I think it's working correctly now https://github.com/materialsproject/pymatgen/actions/runs/14563318347 bravo! Thanks for the quick fix.~~\n\nMy bad, doesn't wait until the workflow finished and jumped to the conclusion:\n> Error: Unable to resolve action `pypa/cibuildwheel@v2.32.2`, unable to find version `v2.32.2`\n\n"
] | 2025-04-20T20:35:25
| 2025-04-20T22:13:16
|
2025-04-20T21:07:53Z
|
MEMBER
|
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### Python version
Python 3.13
### Pymatgen version
current
### Operating system version
_No response_
### Current behavior
See CI release workflow run. 3.13 builds do not work.
### Expected Behavior
3.13 wheels are built and uploaded.
### Minimal example
```Python
```
### Relevant files to reproduce this bug
_No response_
|
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I_kwDOACgets6zPrgz
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[DEV] Update CSV source for vdw/metallic radii data of `core.periodic_table.json` with CRC Handbook
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[
"Looks like some values are manually updated/added directly to JSON in #2122 (and CSV not updated), so likely #4345 might have reverted some changes made in #2122, I would re-apply the update directly to the CSV (and automatically to the JSON) perhaps from a latest version of CRC Handbook \n\ncc @rkingsbury @mkhorton \n\n\n",
"> Looks like some values are manually updated/added directly to JSON in [#2122](https://github.com/materialsproject/pymatgen/pull/2122) (and CSV not updated), so likely [#4345](https://github.com/materialsproject/pymatgen/pull/4345) might have reverted some changes made in [#2122](https://github.com/materialsproject/pymatgen/pull/2122), I would re-apply the update directly to the CSV (and automatically to the JSON) perhaps from a latest version of CRC Handbook\n> \n> cc [@rkingsbury](https://github.com/rkingsbury) [@mkhorton](https://github.com/mkhorton)\n\nHi @DanielYang59 , yes, you are correct! I saw your recent PR in today's release of pymatgen and wrote myself a note to make you aware, but you beat me to it.\n\nI don't think I was aware of the CSV file or understand it's role, but in [#2122](https://github.com/materialsproject/pymatgen/pull/2122) I painstakingly checked all the values in the .`json` files against up to date sources, so I would consider those values to be the more reliable here. Please restore them when you can.",
"\n> in https://github.com/materialsproject/pymatgen/pull/2122 I painstakingly checked all the values in the .json files against up to date sources, so I would consider those values to be the more reliable here. Please restore them when you can.\n\nApologize, I should have checked the history more carefully (but with previous JSON generation mechanism it's a bit hard to track what is going on with the ptable JSON, and that's exactly what motivates #4338\n\nAbsolutely, as I currently don't have access to any CRC handbook (plus the 91st ed is not the latest but still reasonably up-to-date), I would just re-apply all values from #2122\n\n---\n\n> I don't think I was aware of the CSV file or understand it's role\n\nThat's exactly what leads to #4338, before that there were a few parsers which (selectively) generate/update final JSON from various sources (and as such if the JSON get updated directly, it would be inconsistent with [its CSV source](https://github.com/materialsproject/pymatgen/blob/4a3d06780cf32fdd8ad38f480ad588a82860e0e6/dev_scripts/radii.csv) in this case)\n\nhttps://github.com/materialsproject/pymatgen/blob/4a3d06780cf32fdd8ad38f480ad588a82860e0e6/dev_scripts/update_pt_data.py#L94-L127\n\nSo with the new JSON generator in #4338, we could now focus on maintaining the source and leave the JSON being automatically generated as the latter is hardly human-readable nor plays well with version control system. In this case, we could simply update the [`radii.csv`](https://github.com/materialsproject/pymatgen/blob/master/dev_scripts/periodic_table_resources/radii.csv) and rerun the [JSON generator](https://github.com/materialsproject/pymatgen/blob/master/dev_scripts/generate_periodic_table_yaml_json.py) to generate the final JSON (and YAML).\n\n",
"Thanks @DanielYang59 . #4338 does sound like a good thing in principle, just please proceed carefully as you said in the PR. Along similar lines as the VdW radii, I recently updated the valence electron configurations in the .json file. See [#3851](https://github.com/materialsproject/pymatgen/pull/3851). Please make sure those changes remain as well.",
"Hi thanks for the heads up, yes I indeed noticed the difference in #4371 :)"
] | 2025-04-20T21:14:57
| 2025-04-21T19:47:49
|
2025-04-21T19:47:49Z
|
CONTRIBUTOR
|
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#4345 only corrected inconsistency when data exists in BOTH the CSV and JSON, however I noticed some more issues are not corrected **when the data only exist in JSON but not the CSV** (meaning the data source is likely unknown or at least not from the CSV...)
Note the parent hash of #4338 is 4a3d06780cf32fdd8ad38f480ad588a82860e0e6 in case someone want to ensure it's not the new JSON generator the culprit.
|
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I_kwDOACgets6zPwSQ
| 4,371
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"Electronic structure" in ptable YAML (for dev) and JSON (production) are quite different
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[
"Would close this now, now that the new JSON generator uses the one with extra info",
"Ah I see it was updated in #3902"
] | 2025-04-20T22:02:01
| 2025-04-20T22:15:25
|
2025-04-20T22:06:23Z
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CONTRIBUTOR
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repost from #4338 for visibility, "Electronic structure" in ptable YAML (for dev) and JSON (production) are quite different. The hash for the parent of #4338 is 4a3d06780cf32fdd8ad38f480ad588a82860e0e6
For example the electronic structure for Ac in YAML is: https://github.com/materialsproject/pymatgen/blob/4a3d06780cf32fdd8ad38f480ad588a82860e0e6/dev_scripts/periodic_table.yaml#L18
From JSON it's: https://github.com/materialsproject/pymatgen/blob/4a3d06780cf32fdd8ad38f480ad588a82860e0e6/src/pymatgen/core/periodic_table.json#L36-L39
Likely JSON was updated separatedly at some point to include data for other oxidation states other than zero: https://github.com/materialsproject/pymatgen/blob/4a3d06780cf32fdd8ad38f480ad588a82860e0e6/src/pymatgen/core/periodic_table.py#L377
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PR_kwDOACgets6TRadZ
| 4,372
|
Reapply update to ptable vdw radii CSV source and JSON with CRC handbook
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[
"@rkingsbury I believe this PR has reverted #4345 and updated the [radii.csv](https://github.com/materialsproject/pymatgen/blob/master/dev_scripts/periodic_table_resources/radii.csv) accordingly. Would appreciate it if you could help me double check as I currently don't have access to a CRC handbook (we could/should work on the CSV instead of the JSON :D ).\r\n\r\nCurrently the JSON generator is still work in progress and it would not overwrite the `core/periodic_table.json` directly, but rather generate a temporary duplicate at `dev_scripts/periodic_table_resources/_periodic_table.json`. As there's still something inconsistency between the JSON and it's parser in `core/periodic_table.py` to be addressed around how value/unit is handled and I'm working on this.\r\n\r\n---\r\n\r\nThere's a [temporary JSON compare script](https://github.com/materialsproject/pymatgen/blob/master/dev_scripts/periodic_table_resources/_compare_json.py) at `dev_scripts` in case you want to compare two JSONs. From my side, I reverted changes to the JSON in core, and update the CSV then regenerated the temporary JSON. And AFAIK these two are the same.\r\n",
"Thanks @DanielYang59 ! Please connect with me via email and I can share the CRC data with you for double checking.\r\n\r\nNote that I don't recall modifying the metallic radii in my previous PR; only the VdW ones. I'm not exactly sure about the meaning of \"metallic radii\".",
"> Note that I don't recall modifying the metallic radii in my previous PR;\r\n\r\nAh okay I didn't noticed that thanks for remaining me!\r\n\r\nI just tracked down the git history (JSON simply doesn't go well with version control). The \"radii.csv\" was added first in bec79b2803c3fe5939819535ef76375867257550 (already with \"Metallic radii\" data), and #1192 & #1241 directly added \"metallic radii\" to the JSON seemingly from a different source.\r\n\r\nIt's quite weird because the CSV data seems to [come from Wikipedia](https://en.wikipedia.org/wiki/Atomic_radii_of_the_elements_(data_page)#Metallic_radius) and #1192 seems to refering to Wikipedia but ended up adding a different value. For example the metallic radius of K is 2.27 in CSV and wikipedia, but #1192 added a value of 2.381\r\n\r\nI would revert changes to metallic radii to be consistent with CSV until we could verify the source from #1192\r\n",
"> I can share the CRC data with you for double checking.\r\n\r\nThanks for sharing the vdw radii page. I just double checked all vdw radii, and noticed only one difference (could also be the 105th ed CRC handbook updated after the 91st ed):\r\n- vdw radius for Pa is 2.43 in the handbook, 2.1 after #2122 ",
"> > I can share the CRC data with you for double checking.\r\n> \r\n> Thanks for sharing the vdw radii page. I just double checked all vdw radii, and noticed only one difference (could also be the 105th ed CRC handbook after the 91st ed):\r\n> \r\n> * vdw radius for Pa is 2.43 in the handbook, 2.1 after [Update vdW radii to CRC handbook values #2122](https://github.com/materialsproject/pymatgen/pull/2122)\r\n\r\nGreat! Thank you for double checking. I'd go with the latest CRC value (2.43). A better value may have become available since the edition that I used in the previous PR.",
"> I'd go with the latest CRC value (2.43). \r\n\r\nYes it has been applied in 939ea85ee71f901dd0f7020d8f4251a2710347d4"
] | 2025-04-21T08:40:01
| 2025-04-22T08:13:37
|
2025-04-21T19:47:48Z
|
CONTRIBUTOR
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## Summary
- Update ptable vdw radii CSV source, to fix #4370
- [x] Revert #4345 and apply changes to CSV
vdw radii data source:
> John R. Rumble, ed., CRC Handbook of Chemistry and Physics, 105th Edition (Internet Version 2024), CRC Press/Taylor & Francis, Boca Raton, FL.
> If a specific table is cited, use the format: "Physical Constants of Organic Compounds," in CRC Handbook of Chemistry and Physics, 105th Edition (Internet Version 2024), John R. Rumble, ed., CRC Press/Taylor & Francis, Boca Raton, FL.
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[DEV] Add source for "Ground level" and "NMR Quadrupole Moment" to ptable JSON
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[
"link #4374"
] | 2025-04-21T11:40:19
| 2025-04-21T12:01:27
|
2025-04-21T12:01:27Z
|
CONTRIBUTOR
|
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Repost from #4338, it looks like ["Ground level"](https://github.com/materialsproject/pymatgen/blob/ac95b80fc914d237bc31b0e3cd74c1d95f85e135/dev_scripts/update_pt_data.py#L258-L279) (also from `ionization_energies_nist.html`) and ["NMR Quadrupole Moment"](https://github.com/materialsproject/pymatgen/pull/1099) are directly added to JSON without keeping a local copy of the source
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PR_kwDOACgets6TSkR9
| 4,374
|
Separate "Ground level" and "NMR Quadrupole Moment" from `elemental_properties.yaml` for ptable JSON
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[
"close, just do this in #4376"
] | 2025-04-21T11:49:57
| 2025-04-22T18:25:23
|
2025-04-22T18:23:43Z
|
CONTRIBUTOR
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#4373
- [x] Add "NMR Quadrupole Moment" as a separate YAML source
- [x] "ground level" seems to be generated from the same NIST source as ionization energies
|
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Bump nokogiri from 1.18.4 to 1.18.8 in /docs
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[] | 2025-04-22T04:52:05
| 2025-04-22T05:02:46
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Bumps [nokogiri](https://github.com/sparklemotion/nokogiri) from 1.18.4 to 1.18.8.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/sparklemotion/nokogiri/releases">nokogiri's releases</a>.</em></p>
<blockquote>
<h2>v1.18.8 / 2025-04-21</h2>
<h3>Security</h3>
<ul>
<li>[CRuby] Vendored libxml2 is updated to <a href="https://gitlab.gnome.org/GNOME/libxml2/-/releases/v2.13.8">v2.13.8</a> to address CVE-2025-32414 and CVE-2025-32415. See <a href="https://github.com/sparklemotion/nokogiri/security/advisories/GHSA-5w6v-399v-w3cc">GHSA-5w6v-399v-w3cc</a> for more information.</li>
</ul>
<!-- raw HTML omitted -->
<pre><code>36badd2eb281fca6214a5188e24a34399b15d89730639a068d12931e2adc210e nokogiri-1.18.8-aarch64-linux-gnu.gem
664e0f9a77a7122a66d6c03abba7641ca610769a4728db55ee1706a0838b78a2 nokogiri-1.18.8-aarch64-linux-musl.gem
483b5b9fb33653f6f05cbe00d09ea315f268f0e707cfc809aa39b62993008212 nokogiri-1.18.8-arm64-darwin.gem
17de01ca3adf9f8e187883ed73c672344d3dbb3c260f88ffa1008e8dc255a28e nokogiri-1.18.8-arm-linux-gnu.gem
6e6d7e71fc39572bd613a82d528cf54392c3de1ba5ce974f05c832b8187a040b nokogiri-1.18.8-arm-linux-musl.gem
8c7464875d9ca7f71080c24c0db7bcaa3940e8be3c6fc4bcebccf8b9a0016365 nokogiri-1.18.8.gem
41002596960ff854198a20aaeb34cff0d445406d5ad85ba7ca9c3fd0c8f03de0 nokogiri-1.18.8-java.gem
11ab0f76772c5f2d718fb253fca5b74c6ef7628b72bbf8deba6ab1ffc93344cf nokogiri-1.18.8-x64-mingw-ucrt.gem
024cdfe7d9ae3466bba6c06f348fb2a8395d9426b66a3c82f1961b907945cc0c nokogiri-1.18.8-x86_64-darwin.gem
4a747875db873d18a2985ee2c320a6070c4a414ad629da625fbc58d1a20e5ecc nokogiri-1.18.8-x86_64-linux-gnu.gem
ddd735fba49475a395b9ea793bb6474e3a3125b89960339604d08a5397de1165 nokogiri-1.18.8-x86_64-linux-musl.gem
</code></pre>
<h2>v1.18.7 / 2025-03-31</h2>
<h3>Dependencies</h3>
<ul>
<li>[CRuby] Vendored libxml2 is updated to <a href="https://gitlab.gnome.org/GNOME/libxml2/-/releases/v2.13.7">v2.13.7</a>, which is a bugfix release.</li>
</ul>
<!-- raw HTML omitted -->
<pre><code>57a064ab5440814a69a0e040817bd8154adea68a30d2ff2b3aa515a6a06dbb5f nokogiri-1.18.7-aarch64-linux-gnu.gem
3e442dc5b69376e84288295fe37cbb890a21ad816a7e571e5e9967b3c1e30cd3 nokogiri-1.18.7-aarch64-linux-musl.gem
083abb2e9ed2646860f6b481a981485a658c6064caafaa81bf1cda1bada2e9d5 nokogiri-1.18.7-arm64-darwin.gem
337d9149deb5ae01022dff7c90f97bed81715fd586aacab0c5809ef933994c5e nokogiri-1.18.7-arm-linux-gnu.gem
97a26edcc975f780a0822aaf7f7d7427c561067c1c9ee56bd3542960f0c28a6e nokogiri-1.18.7-arm-linux-musl.gem
6b63ff5defe48f30d1d3b3122f65255ca91df2caf5378c6e0482ce73ff46fb31 nokogiri-1.18.7.gem
2cb83666f35619ec59d24d831bf492e49cfe27b112c222330ee929737f42f2eb nokogiri-1.18.7-java.gem
681148fbc918aa5d54933d8b48aeb9462ab708d23409797ed750af961107f72b nokogiri-1.18.7-x64-mingw-ucrt.gem
081d1aa517454ba3415304e2ea51fe411d6a3a809490d0c4aa42799cada417b7 nokogiri-1.18.7-x86_64-darwin.gem
3a0bf946eb2defde13d760f869b61bc8b0c18875afdd3cffa96543cfa3a18005 nokogiri-1.18.7-x86_64-linux-gnu.gem
9d83f8ec1fc37a305fa835d7ee61a4f37899e6ccc6dcb05be6645fa9797605af nokogiri-1.18.7-x86_64-linux-musl.gem
</code></pre>
<h2>v1.18.6 / 2025-03-24</h2>
<h3>Fixed</h3>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/sparklemotion/nokogiri/blob/main/CHANGELOG.md">nokogiri's changelog</a>.</em></p>
<blockquote>
<h2>v1.18.8 / 2025-04-21</h2>
<h3>Security</h3>
<ul>
<li>[CRuby] Vendored libxml2 is updated to <a href="https://gitlab.gnome.org/GNOME/libxml2/-/releases/v2.13.8">v2.13.8</a> to address CVE-2025-32414 and CVE-2025-32415. See <a href="https://github.com/sparklemotion/nokogiri/security/advisories/GHSA-5w6v-399v-w3cc">GHSA-5w6v-399v-w3cc</a> for more information.</li>
</ul>
<h2>v1.18.7 / 2025-03-31</h2>
<h3>Dependencies</h3>
<ul>
<li>[CRuby] Vendored libxml2 is updated to <a href="https://gitlab.gnome.org/GNOME/libxml2/-/releases/v2.13.7">v2.13.7</a>, which is a bugfix release.</li>
</ul>
<h2>v1.18.6 / 2025-03-24</h2>
<h3>Fixed</h3>
<ul>
<li>[JRuby] In HTML documents, <code>Node#attribute</code> now returns the correct attribute. This has been broken, and returning <code>nil</code>, since v1.17.0. (<a href="https://redirect.github.com/sparklemotion/nokogiri/issues/3487">#3487</a>) <a href="https://github.com/flavorjones"><code>@flavorjones</code></a></li>
</ul>
<h2>v1.18.5 / 2025-03-19</h2>
<h3>Fixed</h3>
<ul>
<li>[JRuby] Update JRuby's XML serialization so it outputs namespaces exactly like CRuby. (<a href="https://redirect.github.com/sparklemotion/nokogiri/issues/3455">#3455</a>, <a href="https://redirect.github.com/sparklemotion/nokogiri/issues/3456">#3456</a>) <a href="https://github.com/johnnyshields"><code>@johnnyshields</code></a></li>
</ul>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/sparklemotion/nokogiri/commit/9187f4af0dc3cc7ea439bd4957a2fbfab18f6665"><code>9187f4a</code></a> version bump to v1.18.8</li>
<li><a href="https://github.com/sparklemotion/nokogiri/commit/1deea041e3f359ccef67abadf19f0d634bf473dd"><code>1deea04</code></a> dep: libxml2 to v2.13.8 (branch <code>v1.18.x</code>) (<a href="https://redirect.github.com/sparklemotion/nokogiri/issues/3509">#3509</a>)</li>
<li><a href="https://github.com/sparklemotion/nokogiri/commit/6457fe639359edda9f8817994bc4935abae3e81e"><code>6457fe6</code></a> dep: libxml2 to v2.13.8</li>
<li><a href="https://github.com/sparklemotion/nokogiri/commit/13e8aa4ef52f39d1273d2435bd6bfa98982471ef"><code>13e8aa4</code></a> version bump to v1.18.7</li>
<li><a href="https://github.com/sparklemotion/nokogiri/commit/605699d271ee5d7f58ab29c4550fad42b9cc3d69"><code>605699d</code></a> dep: bump libxml2 to 2.13.7 (v1.18.x backport) (<a href="https://redirect.github.com/sparklemotion/nokogiri/issues/3495">#3495</a>)</li>
<li><a href="https://github.com/sparklemotion/nokogiri/commit/804e59038c4318ebe7ba0dc08105cff2030df415"><code>804e590</code></a> dep: bump libxml2 to 2.13.7</li>
<li><a href="https://github.com/sparklemotion/nokogiri/commit/52bf15b62d4e576292e21ac948030783333f9e0b"><code>52bf15b</code></a> dep(dev): drop Rubocop from JRuby deps</li>
<li><a href="https://github.com/sparklemotion/nokogiri/commit/189769d72627da5f272c7ffcc14cce49d60b9b5a"><code>189769d</code></a> version bump to v1.18.6</li>
<li><a href="https://github.com/sparklemotion/nokogiri/commit/de4982f0cc39995570f5d15d753577bede5804dc"><code>de4982f</code></a> fix(jruby): Node#attribute in HTML documents (v1.18.x) (<a href="https://redirect.github.com/sparklemotion/nokogiri/issues/3492">#3492</a>)</li>
<li><a href="https://github.com/sparklemotion/nokogiri/commit/7d95b0f10cf33c505f11fd7d68d7de8943dda2cd"><code>7d95b0f</code></a> fix(jruby): Node#attribute in HTML documents</li>
<li>Additional commits viewable in <a href="https://github.com/sparklemotion/nokogiri/compare/v1.18.4...v1.18.8">compare view</a></li>
</ul>
</details>
<br />
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Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`.
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<summary>Dependabot commands and options</summary>
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You can trigger Dependabot actions by commenting on this PR:
- `@dependabot rebase` will rebase this PR
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Use new JSON for ptable with better value/unit handling (also slightly faster)
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[
"Is this ready for merging?",
"Yes I believe so as far as I could tell. Tested both consistency of the values and performance, and nothing stands out (except for some changes which I consider to be fixes and have been highlighted as comments).\r\n\r\nCan we merge #4380 first? This PR would automatically drop those in #4380 (as these are not in the source CSV file) but I guess it's better to drop them separately for visibility.\r\n\r\nThanks!"
] | 2025-04-22T14:53:50
| 2025-05-01T21:21:46
|
2025-05-01T21:15:55Z
|
CONTRIBUTOR
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Follow up #4338
- JSON would carry a unit for each property (previously unit would be carried together with the value like `"1.0 kPa"`)
- [x] Avoid hard-coded units in `core.periodic_table.py`
- [x] Avoid post-process values/units on the fly now that it's a static local copy (and store already processed values instead), for example:
https://github.com/materialsproject/pymatgen/blob/39cdbda9fa79c70848dfcf74748a29e2a2ff7acd/src/pymatgen/core/periodic_table.py#L243
- [x] Better `factor` support (currently only for "Coefficient of linear thermal expansion" and "Electrical resistivity")
- [x] Fix `get_and_parse_electronic_affinities` to handle deuterium
- [x] Reapply #4374
---
In #4344 owing to the numerical tolerance used to find "outdated values" (`> 1e-4` IIRC), some more electron affinity would be updated:
```
[electron_affinity][C]: 1.262113612 → 1.262122611
[electron_affinity][Co]: 0.662265 → 0.66225547
[electron_affinity][Cr]: 0.6758412 → 0.67592827
[electron_affinity][Cs]: 0.47163025 → 0.471598338
[electron_affinity][Fe]: 0.15323634 → 0.15323635
[electron_affinity][Ga]: 0.3012011 → 0.30116615
[electron_affinity][I]: 3.05905238 → 3.059046537
[electron_affinity][Mo]: 0.74733 → 0.747238
[electron_affinity][O]: 1.4611053 → 1.461112979
[electron_affinity][P]: 0.7466071 → 0.74660911
[electron_affinity][Re]: 0.06039663 → 0.06039664
[electron_affinity][S]: 2.077104512 → 2.07710426
```
---
### Compare with current pymatgen
See scripts and results in `dev_scripts/periodic_table_resources/compare_current_and_new`, removed in 75d2351fc2e1334373a99c3927615203dda31601 (in case someone want to check it out)
- [x] Double check all differences reported
### A Quick performance benchmark
macos 15.4.1, python 3.12.10
PyPI pymatgen 2025.4.24:
```
Timer unit: 1e-06 s
Total time: 1.0354 s
File: quick_benchmark_element.py
Function: get_all_element_properties at line 34
Line # Hits Time Per Hit % Time Line Contents
==============================================================
34 @profile
35 def get_all_element_properties():
36 1 760220.0 760220.0 73.4 from pymatgen.core.periodic_table import Element
37
38 119 130.0 1.1 0.0 for el in Element:
39 8260 996.0 0.1 0.1 for prop in all_properties:
40 8142 507.0 0.1 0.0 try:
41 8142 273482.0 33.6 26.4 _ = getattr(el, prop)
42 164 25.0 0.2 0.0 except Exception:
43 164 36.0 0.2 0.0 pass
```
This PR (JSON uncompressed):
```
Timer unit: 1e-06 s
Total time: 0.874628 s
File: quick_benchmark_element.py
Function: get_all_element_properties at line 34
Line # Hits Time Per Hit % Time Line Contents
==============================================================
34 @profile
35 def get_all_element_properties():
36 1 639721.0 639721.0 73.1 from pymatgen.core.periodic_table import Element
37
38 119 119.0 1.0 0.0 for el in Element:
39 8260 938.0 0.1 0.1 for prop in all_properties:
40 8142 523.0 0.1 0.1 try:
41 8142 233263.0 28.6 26.7 _ = getattr(el, prop)
42 173 20.0 0.1 0.0 except Exception:
43 173 44.0 0.3 0.0 pass
```
This PR (compressed with `gzip`):
```
Timer unit: 1e-06 s
Total time: 0.736648 s
File: quick_benchmark_element.py
Function: get_all_element_properties at line 34
Line # Hits Time Per Hit % Time Line Contents
==============================================================
34 @profile
35 def get_all_element_properties():
36 1 492745.0 492745.0 66.9 from pymatgen.core.periodic_table import Element
37
38 119 140.0 1.2 0.0 for el in Element:
39 8260 1005.0 0.1 0.1 for prop in all_properties:
40 8142 608.0 0.1 0.1 try:
41 8142 242086.0 29.7 32.9 _ = getattr(el, prop)
42 173 23.0 0.1 0.0 except Exception:
43 173 41.0 0.2 0.0 pass
```
Also tested on my WSL2 Ubuntu 22.04, it's roughly 0.8 s from PyPI and 0.7 s from the PR
|
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| 3,011,681,967
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I_kwDOACgets6zgp6v
| 4,377
|
`PDPlotter`: not showing lowest energy above-hull entry markers in 2D phase diagrams
|
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[] | 2025-04-22T17:24:25
| 2025-04-22T17:25:51
|
MEMBER
|
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### Python version
Python 3.11.11
### Pymatgen version
2025.4.17
### Operating system version
Mac OS X 15.3.2
### Current behavior
When plotting 2D ternary (and 3D quaternary) phase diagrams with `PDPlotter`, if there are multiple above-hull entries for a non-hull composition, the plot will typically show the higher-energy entry (not sure if this always true or just sometimes true) . This leads to the perception that these compositions are further above the hull then they probably should be.
I believe this is an oversight in the plotly marker code (which I wrote a few years back).
### Expected Behavior
If you only take the minimum-energy entries for every composition, you see a more realistic representation of the above-hull compositions.
The differences are subtle in this particular system, but check out Li5Fe5O12, for example. This kind of misleading plotting is worse in larger materials databases.
### Minimal example
```Python
with MPRester() as mpr:
ents = mpr.get_entries_in_chemsys("Li-Fe-O")
min_entries = {}
for ent in ents:
if ent.composition.reduced_formula in min_entries:
if ent.energy_per_atom < min_entries[ent.composition.reduced_formula].energy_per_atom:
min_entries[ent.composition.reduced_formula] = ent
else:
min_entries[ent.composition.reduced_formula] = ent
pd1 = PhaseDiagram(ents).get_plot()
pd2 = PhaseDiagram(min_entries.values()).get_plot()
```
### Relevant files to reproduce this bug
_No response_
|
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| 3,014,100,404
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PR_kwDOACgets6TmVQq
| 4,378
|
Avoid merging if a structure has only one site
|
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[
"Thanks."
] | 2025-04-23T13:50:05
| 2025-04-23T14:11:45
|
2025-04-23T14:11:41Z
|
CONTRIBUTOR
|
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## Summary
This PR fixes an error that occurs when calling `merge_sites` on a structure with only one site. For example:
```python
from pymatgen.core import Structure, Lattice
cu = Structure(Lattice.from_parameters(2.5, 2.5, 2.5, 60, 60, 60), species=["Cu"], coords=[[0.0, 0.0, 0.0]])
cu.merge_sites(mode="delete")
```
This currently raises the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/mrok/work/pymatgen/.venv/lib/python3.11/site-packages/pymatgen/core/structure.py", line 4840, in merge_sites
clusters = fcluster(linkage(squareform((dist_mat + dist_mat.T) / 2)), tol, "distance")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/mrok/work/pymatgen/.venv/lib/python3.11/site-packages/scipy/cluster/hierarchy.py", line 1033, in linkage
n = int(distance.num_obs_y(y))
^^^^^^^^^^^^^^^^^^^^^
File "/Users/mrok/work/pymatgen/.venv/lib/python3.11/site-packages/scipy/spatial/distance.py", line 2742, in num_obs_y
raise ValueError("The number of observations cannot be determined on "
ValueError: The number of observations cannot be determined on an empty distance matrix.
```
The root cause is that the distance matrix is a 1×1 array `[[0.0]]`, which leads to a failure in `scipy.cluster.hierarchy.linkage`.
This PR resolves the issue by skipping the merging step and returning the original structure unchanged when only one site is present.
A unit test is also added to verify this behavior and prevent regressions.
## Todos
No more todos.
## Checklist
- [x] Google format doc strings added. Check with `ruff`.
- [x] Type annotations included. Check with `mypy`.
- [x] Tests added for new features/fixes.
- [x] If applicable, new classes/functions/modules have [`duecredit`](https://github.com/duecredit/duecredit) `@due.dcite` decorators to reference relevant papers by DOI ([example](https://github.com/materialsproject/pymatgen/blob/91dbe6ee9ed01d781a9388bf147648e20c6d58e0/pymatgen/core/lattice.py#L1168-L1172))
Tip: Install `pre-commit` hooks to auto-check types and linting before every commit:
```sh
pip install -U pre-commit
pre-commit install
```
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I_kwDOACgets6zs-Lu
| 4,379
|
`MPRester` Doc Issues
|
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[] | null |
[
"I think I know what is happening. Basically, I was getting property_data from the summary interface, which is the so-called \"best\" data available. In many cases, this is the r2scan data. That's why there is a mismatch.\n\nI have modified the code to just get it from the thermo interface to be consistent. There are unfortunately fewer properties available in the thermo interface, but there is no way to get consistent data via the summary interface."
] | 2025-04-23T18:22:11
| 2025-04-23T19:28:48
|
2025-04-23T19:28:48Z
|
CONTRIBUTOR
|
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### Python version
3.10
### Pymatgen version
2025.4.20
### Operating system version
_No response_
### Current behavior
Related to https://github.com/materialsproject/pymatgen/issues/4366
I have tested and those issues have now been resolved. However, I have seen some remaining issues:
The `(uncorrected)_energy_per_atom` values are off in the returned entry data dicts (around an order of magnitude larger than they should be). See MWE below.
Also, the formation energies given don't seem to match either the corrected or uncorrected values. Based on the energies given in the returned entries (see below), the formation energy of the CdTe entry should be either `(-5.4048 - -0.9093 - -3.1433 - -0.2110)/2 = -0.5706` eV/atom if corrected, `(-4.9828 - -0.9093 - -3.1433)/2 = -0.4651` eV/atom if uncorrected, but the listed value is `-0.5607` eV/atom.
### Expected Behavior
...
### Minimal example
```Python
from pymatgen.ext.matproj import MPRester
with MPRester() as mpr:
MP_full_pd_entries = mpr.get_entries_in_chemsys("Cd-Te", property_data=["energy_above_hull", "energy_per_atom", "formation_energy_per_atom","uncorrected_energy_per_atom"])
for entry in MP_full_pd_entries:
if entry.data["energy_above_hull"] == 0:
print(entry)
```
### Relevant files to reproduce this bug
Output:
```
mp-406-GGA ComputedStructureEntry - Cd1 Te1 (CdTe)
Energy (Uncorrected) = -4.9828 eV (-2.4914 eV/atom)
Correction = -0.4220 eV (-0.2110 eV/atom)
Energy (Final) = -5.4048 eV (-2.7024 eV/atom)
Energy Adjustments:
MP2020 anion correction (Te): -0.4220 eV (-0.2110 eV/atom)
Parameters:
potcar_spec = [{'titel': 'PAW_PBE Cd 06Sep2000', 'hash': '0506b2d0ac28d5fe2b5ced77a701aa86', 'summary_stats': None}, {'titel': 'PAW_PBE Te 08Apr2002', 'hash': '72719856e22fb1d3032df6f96d98a0f2', 'summary_stats': None}]
run_type = GGA
is_hubbard = False
hubbards = None
Data:
oxide_type = None
aspherical = True
last_updated = 2024-11-21 20:25:47.504437+00:00
task_id = mp-2293120
material_id = mp-406
oxidation_states = {'Cd': 2.0, 'Te': -2.0}
license = BY-C
run_type = GGA
uncorrected_energy_per_atom = -23.321846005
energy_per_atom = -23.321846005
formation_energy_per_atom = -0.5607400050000031
energy_above_hull = 0.0
mp-19-GGA ComputedStructureEntry - Te3 (Te)
Energy (Uncorrected) = -9.4299 eV (-3.1433 eV/atom)
Correction = 0.0000 eV (0.0000 eV/atom)
Energy (Final) = -9.4299 eV (-3.1433 eV/atom)
Energy Adjustments:
None
Parameters:
potcar_spec = [{'titel': 'PAW_PBE Te 08Apr2002', 'hash': '72719856e22fb1d3032df6f96d98a0f2', 'summary_stats': None}]
run_type = GGA
is_hubbard = False
hubbards = None
Data:
oxide_type = None
aspherical = True
last_updated = 2024-11-21 22:51:35.025069+00:00
task_id = mp-2252848
material_id = mp-19
oxidation_states = {'Te': 0.0}
license = BY-C
run_type = GGA
uncorrected_energy_per_atom = -25.417224309999998
energy_per_atom = -25.417224309999998
formation_energy_per_atom = 0.0
energy_above_hull = 0.0
mp-94-GGA ComputedStructureEntry - Cd2 (Cd)
Energy (Uncorrected) = -1.8186 eV (-0.9093 eV/atom)
Correction = 0.0000 eV (0.0000 eV/atom)
Energy (Final) = -1.8186 eV (-0.9093 eV/atom)
Energy Adjustments:
None
Parameters:
potcar_spec = [{'titel': 'PAW_PBE Cd 06Sep2000', 'hash': '0506b2d0ac28d5fe2b5ced77a701aa86', 'summary_stats': None}]
run_type = GGA
is_hubbard = False
hubbards = None
Data:
oxide_type = None
aspherical = True
last_updated = 2024-11-21 20:23:13.913283+00:00
task_id = mp-1792425
material_id = mp-94
oxidation_states = {'Cd': 0.0}
license = BY-C
run_type = GGA
uncorrected_energy_per_atom = -20.10498769
energy_per_atom = -20.10498769
formation_energy_per_atom = 0.0
energy_above_hull = 0.0
```
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| 3,020,271,104
|
PR_kwDOACgets6T7Pm7
| 4,380
|
Drop unknown source metallic radius from ptable JSON
|
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[
"close as already included in #4376"
] | 2025-04-25T14:45:29
| 2025-05-01T21:21:41
|
2025-05-01T21:21:23Z
|
CONTRIBUTOR
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### Summary
Follow up #4345, drop unknown source metallic radius from ptable JSON for the following elements:
```
[metallic_radius][Ac]: 1.878 → None
[metallic_radius][Bi]: 1.82 → None
[metallic_radius][Br]: 1.14 → None
[metallic_radius][Ge]: 1.39 → None
[metallic_radius][Pb]: 1.75 → None
[metallic_radius][Po]: 1.53 → None
[metallic_radius][Ra]: 2.293 → None
[metallic_radius][Sb]: 1.61 → None
[metallic_radius][Sn]: 1.58 → None
```
The [radii CSV](https://github.com/materialsproject/pymatgen/blob/master/dev_scripts/periodic_table_resources/radii.csv) doesn't have recordings for them, nor [the wikipedia](https://en.wikipedia.org/wiki/Atomic_radii_of_the_elements_(data_page)) (Br is not even a metal). I wouldn't say the wikipedia is the best source, but we could go back and update them with more reliable source like CSC handbook after #4376
This as a separate PR for visibility, don't want to "sneakily" update data in #4376
|
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| 3,023,326,078
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PR_kwDOACgets6UFTr3
| 4,381
|
Refactor PhaseDiagram: Simplify Entries Serialization
|
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[] | null |
[
"## 👋 Hello from RepoBird.ai!\n\nI'm the founder of **RepoBird.ai**, an AI Software Engineer Agent that integrates seamlessly as a GitHub App.\n\nThis Pull Request was automatically generated by our agent, **RepoBirdBot**, to address issue #3940. This issue was marked as a `good first issue` and appeared to be unassigned.\n\n**Why this PR?**\n\n* We're currently testing **RepoBird** by offering automated fixes for open-source projects like yours.\n* Our goal is to help maintainers and provide useful contributions.\n* We greatly value feedback from maintainers and early users during this phase.\n\n**Your Feedback Matters:**\n\nPlease review the proposed changes. If this type of automated contribution isn't suitable for your repository, or if this specific PR isn't helpful, just let me know! I'll close it, make any improvements and unlist these PRs – no worries.\n\n---\n\n**Interested in learning more?**\n\n* Visit [**Repobird.ai**](https://repobird.ai) to explore our Agentic AI-driven code automation.\n* Install the [**RepoBird GitHub App**](https://github.com/apps/repobird).\n\nThank you!"
] | 2025-04-27T21:13:02
| 2025-04-29T15:10:23
|
2025-04-29T15:10:23Z
|
NONE
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This PR simplifies the serialization process in the `PhaseDiagram` class by removing the redundant commented-out code for the `all_entries` key in the dictionary returned by `as_dict()`. Previously, the code left a commented-out line that serialized `all_entries` which was redundant and caused confusion.
The changes include:
- Removing the commented-out serialization code for `all_entries`.
- Ensuring that `all_entries` is serialized consistently with other lists (such as `elements`).
This cleanup helps maintain code clarity and consistency, aligning with the project's refactoring goals in resolving issue #3940.
Please review and let me know if further changes are needed.
---
*Created with [Repobird.ai](https://repobird.ai) 📦🐦*
|
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I_kwDOACgets60NFjX
| 4,382
|
Note: Inconsistent use of "magnetization" vs "magnetisation"
|
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[] | null |
[
"Though I am not responsible for the magnetisation part of the code, as a UK-educated man, I must just say that Americans butcher the English language. :-) \n\n@mkhorton I think you might want to fix these? Probably just a rename with an additonal property method where appropriate to maintain backwards compatibility with deprecation timeline."
] | 2025-04-27T21:20:28
| 2025-04-28T17:47:05
|
CONTRIBUTOR
|
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Just flagging, obviously a very minor thing, but for most parts of the code; "magnetization" (US english) is used, but there are a small minority of cases which use "magnetisation", such as:
https://github.com/materialsproject/pymatgen/blob/93be6fb1ae3c5066164526e9f487c0b4114169e9/src/pymatgen/io/vasp/outputs.py#L390
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I_kwDOACgets60NHZ2
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|
Some Remaining Issues with `MPRester`
|
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[
"Thanks for reporting this. I just pushed a fix.",
"Great, thank you! I've confirmed it's all now working as expected, and passing all our tests."
] | 2025-04-27T21:37:38
| 2025-04-27T23:29:29
|
2025-04-27T22:53:29Z
|
CONTRIBUTOR
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Thank you very much for the recent updates @shyuep -- `MPRester()` is now mostly working as before for our target use cases. There are two remaining issues we have been running into:
- When using `summary_data` with systems with wide chemical systems, a URl error is returned. e.g. when looking for potential competing phases to the Na2FePO4F battery cathode:
```python
from pymatgen.ext.matproj import MPRester
with MPRester() as mpr:
entries = mpr.get_entries_in_chemsys("Na-Fe-P-O-F")
```
runs fine, returning 1144 entries, but:
```python
with MPRester() as mpr:
entries = mpr.get_entries_in_chemsys("Na-Fe-P-O-F", summary_data=["band_gap", "total_magnetization"])
```
breaks with:
```
MPRestError Traceback (most recent call last)
File ~/Packages/pymatgen/src/pymatgen/ext/matproj.py:176, in MPRester.request(self, sub_url, payload, method, mp_decode)
175 else:
--> 176 raise MPRestError(f"REST query returned with error status code {response.status_code}")
177 all_data.extend(data["data"])
MPRestError: REST query returned with error status code 414
During handling of the above exception, another exception occurred:
MPRestError Traceback (most recent call last)
Cell In[39], line 4
1 from pymatgen.ext.matproj import MPRester
3 with MPRester() as mpr:
----> 4 entries = mpr.get_entries_in_chemsys("Na-Fe-P-O-F", summary_data=MPRester_summary_data)
File ~/Packages/pymatgen/src/pymatgen/ext/matproj.py:469, in MPRester.get_entries_in_chemsys(self, elements, *args, **kwargs)
466 chemsys.append("-".join(sorted(els)))
467 criteria = ",".join(chemsys)
--> 469 return self.get_entries(criteria, *args, **kwargs)
File ~/Packages/pymatgen/src/pymatgen/ext/matproj.py:420, in MPRester.get_entries(self, criteria, compatible_only, property_data, summary_data, **kwargs)
417 entries = MaterialsProject2020Compatibility().process_entries(entries, clean=True)
419 if summary_data:
--> 420 edata = self.search(
421 "summary",
422 material_ids=[e.data["material_id"] for e in entries],
423 _fields=[*summary_data, "material_id"],
424 )
425 mapped_data = {d["material_id"]: {k: v for k, v in d.items() if k != "material_id"} for d in edata}
426 for e in entries:
File ~/Packages/pymatgen/src/pymatgen/ext/matproj.py:216, in MPRester.search(self, doc, **kwargs)
214 get = "&".join(params)
215 logger.info(f"query={get}")
--> 216 return self.request(f"materials/{doc}/?{get}", payload=criteria)
File ~/Packages/pymatgen/src/pymatgen/ext/matproj.py:183, in MPRester.request(self, sub_url, payload, method, mp_decode)
181 except Exception as ex:
182 msg = f"{ex}. Content: {response.content}" if hasattr(response, "content") else str(ex)
--> 183 raise MPRestError(msg)
184 return all_data
MPRestError: REST query returned with error status code 414. Content: b'URI too long\n'
```
From digging around, it seemed like `mp-api` has some ways of handling cases of long URls (https://github.com/materialsproject/api/blob/252e68259ca06124663985ad3de19538f3043e06/mp_api/client/core/client.py#L584).
- For cases where one searches for a composition which is not present in the Materials Project database, the search hangs for very long when `summary_data` is used (as we would normally do, to access band gap, total magnetization etc). e.g.
```python
from pymatgen.ext.matproj import MPRester
with MPRester() as mpr:
entries = mpr.get_entries("Cu2SiSe4")
```
runs in 0.1s and returns `[]` as expected, but:
```python
with MPRester() as mpr:
mpr.get_entries("Cu2SiSe4", summary_data=["band_gap"])
```
takes about a minute, before returning nothing. The traceback from interrupting this while it was hanging is copied below, showing where the hang is coming from:
```
File ~/Packages/pymatgen/src/pymatgen/ext/matproj.py:420, in MPRester.get_entries(self, criteria, compatible_only, property_data, summary_data, **kwargs)
417 entries = MaterialsProject2020Compatibility().process_entries(entries, clean=True)
419 if summary_data:
--> 420 edata = self.search(
421 "summary",
422 material_ids=[e.data["material_id"] for e in entries],
423 _fields=[*summary_data, "material_id"],
424 )
425 mapped_data = {d["material_id"]: {k: v for k, v in d.items() if k != "material_id"} for d in edata}
426 for e in entries:
File ~/Packages/pymatgen/src/pymatgen/ext/matproj.py:216, in MPRester.search(self, doc, **kwargs)
214 get = "&".join(params)
215 logger.info(f"query={get}")
--> 216 return self.request(f"materials/{doc}/?{get}", payload=criteria)
File ~/Packages/pymatgen/src/pymatgen/ext/matproj.py:172, in MPRester.request(self, sub_url, payload, method, mp_decode)
170 response = self.session.post(actual_url, data=payload, verify=True)
171 else:
--> 172 response = self.session.get(actual_url, params=payload, verify=True)
173 if response.status_code in [200, 400]:
174 data = json.loads(response.text, cls=MontyDecoder) if mp_decode else json.loads(response.text)
File ~/miniconda3/lib/python3.10/site-packages/requests/sessions.py:602, in Session.get(self, url, **kwargs)
594 r"""Sends a GET request. Returns :class:`Response` object.
595
596 :param url: URL for the new :class:`Request` object.
597 :param \*\*kwargs: Optional arguments that ``request`` takes.
598 :rtype: requests.Response
599 """
601 kwargs.setdefault("allow_redirects", True)
--> 602 return self.request("GET", url, **kwargs)
File ~/miniconda3/lib/python3.10/site-packages/requests/sessions.py:589, in Session.request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
584 send_kwargs = {
585 "timeout": timeout,
586 "allow_redirects": allow_redirects,
587 }
588 send_kwargs.update(settings)
--> 589 resp = self.send(prep, **send_kwargs)
591 return resp
File ~/miniconda3/lib/python3.10/site-packages/requests/sessions.py:703, in Session.send(self, request, **kwargs)
700 start = preferred_clock()
702 # Send the request
--> 703 r = adapter.send(request, **kwargs)
705 # Total elapsed time of the request (approximately)
706 elapsed = preferred_clock() - start
File ~/miniconda3/lib/python3.10/site-packages/requests/adapters.py:589, in HTTPAdapter.send(self, request, stream, timeout, verify, cert, proxies)
586 timeout = TimeoutSauce(connect=timeout, read=timeout)
588 try:
--> 589 resp = conn.urlopen(
590 method=request.method,
591 url=url,
592 body=request.body,
593 headers=request.headers,
594 redirect=False,
595 assert_same_host=False,
596 preload_content=False,
597 decode_content=False,
598 retries=self.max_retries,
599 timeout=timeout,
600 chunked=chunked,
601 )
603 except (ProtocolError, OSError) as err:
604 raise ConnectionError(err, request=request)
File ~/miniconda3/lib/python3.10/site-packages/urllib3/connectionpool.py:793, in HTTPConnectionPool.urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)
790 response_conn = conn if not release_conn else None
792 # Make the request on the HTTPConnection object
--> 793 response = self._make_request(
794 conn,
795 method,
796 url,
797 timeout=timeout_obj,
798 body=body,
799 headers=headers,
800 chunked=chunked,
801 retries=retries,
802 response_conn=response_conn,
803 preload_content=preload_content,
804 decode_content=decode_content,
805 **response_kw,
806 )
808 # Everything went great!
809 clean_exit = True
File ~/miniconda3/lib/python3.10/site-packages/urllib3/connectionpool.py:537, in HTTPConnectionPool._make_request(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)
535 # Receive the response from the server
536 try:
--> 537 response = conn.getresponse()
538 except (BaseSSLError, OSError) as e:
539 self._raise_timeout(err=e, url=url, timeout_value=read_timeout)
File ~/miniconda3/lib/python3.10/site-packages/urllib3/connection.py:466, in HTTPConnection.getresponse(self)
463 from .response import HTTPResponse
465 # Get the response from http.client.HTTPConnection
--> 466 httplib_response = super().getresponse()
468 try:
469 assert_header_parsing(httplib_response.msg)
File ~/miniconda3/lib/python3.10/http/client.py:1375, in HTTPConnection.getresponse(self)
1373 try:
1374 try:
-> 1375 response.begin()
1376 except ConnectionError:
1377 self.close()
File ~/miniconda3/lib/python3.10/http/client.py:318, in HTTPResponse.begin(self)
316 # read until we get a non-100 response
317 while True:
--> 318 version, status, reason = self._read_status()
319 if status != CONTINUE:
320 break
File ~/miniconda3/lib/python3.10/http/client.py:279, in HTTPResponse._read_status(self)
278 def _read_status(self):
--> 279 line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
280 if len(line) > _MAXLINE:
281 raise LineTooLong("status line")
File ~/miniconda3/lib/python3.10/socket.py:705, in SocketIO.readinto(self, b)
703 while True:
704 try:
--> 705 return self._sock.recv_into(b)
706 except timeout:
707 self._timeout_occurred = True
File ~/miniconda3/lib/python3.10/ssl.py:1274, in SSLSocket.recv_into(self, buffer, nbytes, flags)
1270 if flags != 0:
1271 raise ValueError(
1272 "non-zero flags not allowed in calls to recv_into() on %s" %
1273 self.__class__)
-> 1274 return self.read(nbytes, buffer)
1275 else:
1276 return super().recv_into(buffer, nbytes, flags)
File ~/miniconda3/lib/python3.10/ssl.py:1130, in SSLSocket.read(self, len, buffer)
1128 try:
1129 if buffer is not None:
-> 1130 return self._sslobj.read(len, buffer)
1131 else:
1132 return self._sslobj.read(len)
```
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| 3,033,030,455
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I_kwDOACgets60yF83
| 4,384
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[] | 2025-05-01T02:38:55
| 2025-05-01T02:38:55
|
NONE
|
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# Issue Metrics
| Metric | Average | Median | 90th percentile |
| --- | --- | --- | ---: |
| Time to first response | 1 day, 9:59:31 | 2:47:08 | 1 day, 23:31:55 |
| Time to close | 2 days, 0:11:47 | 2:34:48 | 6 days, 4:54:35 |
| Time to answer | None | None | None |
| Metric | Count |
| --- | ---: |
| Number of items that remain open | 4 |
| Number of items closed | 11 |
| Number of most active mentors | 0 |
| Total number of items created | 15 |
| Title | URL | Author | Time to first response | Time to close | Time to answer |
| --- | --- | --- | --- | --- | --- |
| Some Remaining Issues with `MPRester` | https://github.com/materialsproject/pymatgen/issues/4383 | [kavanase](https://github.com/kavanase) | 1:15:59 | 1:15:51 | None |
| Note: Inconsistent use of "magnetization" vs "magnetisation" | https://github.com/materialsproject/pymatgen/issues/4382 | [kavanase](https://github.com/kavanase) | 20:26:36 | None | None |
| `MPRester` Doc Issues | https://github.com/materialsproject/pymatgen/issues/4379 | [kavanase](https://github.com/kavanase) | 1:06:10 | 1:06:37 | None |
| `PDPlotter`: not showing lowest energy above-hull entry markers in 2D phase diagrams | https://github.com/materialsproject/pymatgen/issues/4377 | [mattmcdermott](https://github.com/mattmcdermott) | None | None | None |
| [DEV] Add source for "Ground level" and "NMR Quadrupole Moment" to ptable JSON | https://github.com/materialsproject/pymatgen/issues/4373 | [DanielYang59](https://github.com/DanielYang59) | None | 0:21:08 | None |
| "Electronic structure" in ptable YAML (for dev) and JSON (production) are quite different | https://github.com/materialsproject/pymatgen/issues/4371 | [DanielYang59](https://github.com/DanielYang59) | None | 0:04:22 | None |
| [DEV] Update CSV source for vdw/metallic radii data of `core.periodic_table.json` with CRC Handbook | https://github.com/materialsproject/pymatgen/issues/4370 | [DanielYang59](https://github.com/DanielYang59) | 3:31:49 | 22:32:52 | None |
| Wheel build in Py3.13 does not work | https://github.com/materialsproject/pymatgen/issues/4369 | [shyuep](https://github.com/shyuep) | 0:21:05 | 0:32:28 | None |
| `MPRester` Issues | https://github.com/materialsproject/pymatgen/issues/4366 | [kavanase](https://github.com/kavanase) | 0:21:18 | 3:57:15 | None |
| The strange display style of bar (-) on next-gen.materialsproject.org. | https://github.com/materialsproject/pymatgen/issues/4364 | [hongyi-zhao](https://github.com/hongyi-zhao) | 2:34:48 | 2:34:48 | None |
| The constraints changed after switch back and forth from ase atoms | https://github.com/materialsproject/pymatgen/issues/4354 | [yaoyi92](https://github.com/yaoyi92) | 21:00:09 | 22:25:41 | None |
| Consistent serialization method naming (`as_dict` preferred over `to_dict`?) | https://github.com/materialsproject/pymatgen/issues/4351 | [DanielYang59](https://github.com/DanielYang59) | 2:59:29 | 6 days, 4:54:35 | None |
| ICOHPLIST Parser in pymatgen Does Not Handle Missing Trailing Newline (Ref: lobsterpy#389) | https://github.com/materialsproject/pymatgen/issues/4349 | [alibh95](https://github.com/alibh95) | None | None | None |
| WAVECAR read error: "ValueError: Incorrect vasp_type=None" | https://github.com/materialsproject/pymatgen/issues/4346 | [AyeshaUlde](https://github.com/AyeshaUlde) | 11 days, 22:17:49 | None | None |
| Monthly issue metrics report | https://github.com/materialsproject/pymatgen/issues/4340 | [github-actions[bot]](https://github.com/github-actions[bot]) | None | 13 days, 14:23:59 | None |
_This report was generated with the [Issue Metrics Action](https://github.com/github/issue-metrics)_
Search query used to find these items: `repo:materialsproject/pymatgen is:issue created:2025-04-01..2025-04-30 -reason:"not planned"`
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| 3,033,533,130
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| 4,385
|
`orjson` for much faster JSON operations?
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[] | null |
[
"After posting this \"proposal\", I realized two issues:\n- The saving/loading process is more or less detatched from the serialization (`as_dict`), so one could always install `orjson` locally without any change needed from pymatgen's side.\n- For some processes where the `json.dumps` is integrated, very likely a custom JSON encoder/decoder is needed (e.g. `MontyEncoder`). Looks like [`orjson` natively support serialization of np.array datetime and dataclass](https://github.com/ijl/orjson?tab=readme-ov-file#orjson) (but I haven't tested myself, not sure if results is consistent with `MontyEncoder`), also there is [`default` to support arbitary type](https://github.com/ijl/orjson?tab=readme-ov-file#default) similar to the one from monty encoder:\n > It serializes [dataclass](https://github.com/ijl/orjson?tab=readme-ov-file#dataclass), [datetime](https://github.com/ijl/orjson?tab=readme-ov-file#datetime), [numpy](https://github.com/ijl/orjson?tab=readme-ov-file#numpy), and [UUID](https://github.com/ijl/orjson?tab=readme-ov-file#uuid) instances natively.\n\nBut in any case I'm happy to give this a try \n",
"While I don't have a problem with this in principle as long as orjson is done as an optional import, e.g.,\n\n```\ntry:\n import orjson as json\nexcept ImportError:\n import json\n```\n\nthis only works when you don't need a custom encoder like MontyEncoder or MontyDecoder. This is severely limiting. Now, there are ways to get around that of course. But I think it is not worth the effort. People who really need to work that HT workflow can implement those hacks themselves (e.g., preconverting to dicts and then using orjson to do the dump). The majority of people are dumping a few structures and the difference between 2 ms and 10 ms is not going to be noticeable. \n\nNote: I attempted to do this in Monty and I concluded it is too much complexity to be worth it.\n",
"I would also advocate for a more practical view of optimizations. The most important optimizations are those that serve > 80% of users and result in at least a 10-20%, preferably order of magnitude speed improvements, with almost no backwards incompatibility. E.g., optimizing the nearest neighbor finding code or the recent PR by @kavanase that reduced vasprun parsing times AND simplified the code by removing Incar.get calls. \n\nAnything that serves esoteric needs and/or result in small-ish speed increases is not worth our time, especially if code complexity has to significantly increase. Your code suggests that for 1000 structures, you are saving 1s of time. Very few people are going to work with anything more than 10000 structures and loading / dumping is a one time affair. For people who want to work with 1-100 million datasets, they have the skills to optimize for their own use case.",
"> The most important optimizations are those that serve > 80% of users and result in at least a 10-20%, preferably order of magnitude speed improvements, with almost no backwards incompatibility.\n\nI definitely agree with this. If it's of any use to note; the biggest `pymatgen` runtime bottlenecks in our workflows (which I think is similar to most users) are `Vasprun` parsing (better now but even faster would be nice) and `StructureMatcher` utilities (which has been discussed here: https://github.com/materialsproject/pymatgen/issues/2593)",
"> The most important optimizations are those that serve > 80% of users and result in at least a 10-20%, preferably order of magnitude speed improvements, with almost no backwards incompatibility.\n\nI totally agree with you that an \"ideal\" optimization should bring the most performance benefit to most user while adding minimal code complexity nor backwards incompatibility.\n\nIn this regard replacing `json` with `orjson` seem to tick most of the boxes in my opinion:\n- Speed benefit (quote [`orjson` doc](https://github.com/ijl/orjson?tab=readme-ov-file#orjson)): \n > [orjson.dumps()](https://github.com/ijl/orjson?tab=readme-ov-file#serialize) is something like 10x as fast as json, \n > [orjson.loads()](https://github.com/ijl/orjson?tab=readme-ov-file#deserialize) is something like 2x as fast as json\n- Applicability: I agree loading millions of Structure might be a niche need, but `orjson` would speed up many more JSON related workload across the code base. This is especially relevant given how extensively JSON is used in pymatgen, and considering pymatgen also serves as the backend for the Materials Project web application (if I understand correctly).\n- Code change: for those don't need a custom encoder/decoder, this would largely be a drop-in replacement (assuming it could be a required dependency otherwise would require a lot of checking for `orjson` availability)\n\n> this only works when you don't need a custom encoder like MontyEncoder or MontyDecoder\n\nYes this is a big limiting factor for this migration and I haven't got much good thoughts on this (but still we could leave them as is):\n- `orjson` natively support [dataclass](https://github.com/ijl/orjson?tab=readme-ov-file#dataclass), [datetime](https://github.com/ijl/orjson?tab=readme-ov-file#datetime), [numpy](https://github.com/ijl/orjson?tab=readme-ov-file#numpy), and [UUID](https://github.com/ijl/orjson?tab=readme-ov-file#uuid), so if some instance only needs a custom encoder/decoder for these datatypes, we could just get rid of that\n\n---\n\n> the biggest pymatgen runtime bottlenecks in our workflows (which I think is similar to most users) are Vasprun parsing (better now but even faster would be nice) and StructureMatcher utilities \n\nHi @kavanase thanks for pointing out the bottleneck in your workflow, this would be immensely helpful for people like me who want to tweak some performance but don't know where to get started :D\n\n",
"I am aware json is being used in the code base. I am merely pointing out that the performance gains are small when your datasets are not large. For example, the No. 1 most read file is probably periodic_table.json.\n```\n%timeit -n 100 orjson.loads(open(\"periodic_table.json\").read())\n603 μs ± 46.2 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n\n%timeit -n 100 json.load(open(\"periodic_table.json\"))\n1.13 ms ± 22 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n```\n\nOn paper, 1.13 ms is definitely a 2x improvement over 603 us. Will anyone feel an improvement of 603 us? Probably not.\n\nBut this is one use case where you can easily do a drop-in support for orjson without too much issue. ",
"One last thing: a major issue I have with orjson is that they don't even support simple dump / load from IO streams, in addition lack of support custom encoders/decoders. That's actually very odd given how easy it is to support those. What it means is that we always have to dump to string and then write it separately.\n\nIf you want to find alternatives, here they are. https://catnotfoundnear.github.io/finding-the-fastest-python-json-library-on-all-python-versions-8-compared.html\n\nFinally, feel free to add orjson support wherever it is going to be trivial. But again, I wouldn't spend too much time on this. I actually think there really aren't that many locations in the code where we are explicitly dumping json.",
"@kavanase You will be pleased to know that with the recent [commit](https://github.com/materialsproject/pymatgen/commit/191e6bbeb79100d64d82c8411f5ee9dd05ee6a48), I sped up Vasprun parsing by ~33% using lxml.\n\n```\nVasprun(\"tests/files/io/vasp/outputs/vasprun.r2scan.xml.gz\")\n```\n\nwhich is quite small, took about 0.6s with Python's stdlib ElementTree, but 0.4s with lxml's ElementTree.",
"Ah that would be very nice! I've wondered before if there was a faster version of XML `ElementTree` available somewhere...\n\nI've just run a few tests, and unfortunately seem to find that for large vasprun files, this seems to decrease parsing speed.\nParsing the attached Vasprun (from a SOC defect supercell calculation) 10 times on my MacBook takes 38-39s with the previous code, but 44-48s with the new `lxml` code (running this parsing loop 5 times repeatedly to make sure).\n\n[vasprun.xml.gz](https://github.com/user-attachments/files/20005082/vasprun.xml.gz)\n\nI tried some profiling with `cProfile` but `lxml` seems to block its recording to identify the bottleneck",
"Hmm really? By all accounts, lxml should be faster. Let me investigate a bit. ",
"Yeah I was surprised, but those are the numbers I was consistently getting with that large vasprun file",
"I just had a quick test on my WSL2 Ubuntu 22.04 machine in case it's some issue with specific platform:\n\n- on 2025.4.24: `Average over 5 runs (import + parse): 3.902 seconds`\n- on 2025.5.1: `Average over 5 runs (import + parse): 6.887 seconds`\n\nI didn't do any profiling either so not sure where the regression comes from (hopefully not from the new ptable JSON handler LOL).\n\nThanks for the helpful discussion in any case and enjoy the holiday :D\n\n",
"When I tested, I checked out the specific `lxml` commit (before the ptable JSON stuff) so it shouldn't be that at least. `lxml` doesn't seem to play well with `cProfile` for some reason, as it just doesn't show what it's doing, whereas with XML `ElementTree` it showed the `iterator`, `feed` and `read_events` functions as taking the bulk of the IO time.",
"@kavanase I found that setting `remove_blank_text=True` yielded a signfiicant increase in speed. Can you try the latest version on master.",
"I tested again (with that vasprun shared above), and it's better. The `lxml` parsing time is now 35.5-38 seconds, versus 38-40s originally (and 44-48s with `lxml` but without `remove_blank_text=True`). So a bit faster than the original code now!",
"On my machine is slightly faster but still slower than before: `Average over 5 runs (import + parse): 5.640 seconds`\n\nI usually work on MacOS machine too, but I tried to report performance on Linux (even it's WSL2) as I guess most people who're serious about performance might be running on Linux :D",
"I have reverted the lxml since it is a bit slower in many instances and the additional complexity of figuring out when it is faster and when it is slower isn't worth adding a dependency."
] | 2025-05-01T09:39:38
| 2025-05-02T18:16:27
|
CONTRIBUTOR
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I was trying to optimize the performance for a high-throughput use case (proposed by @janosh) where one might need to load/save large quantities of `Structure` as JSON, and I'm currently starting with the saving phase of the workflow.
A quick line profiling reveals two significant bottlenecks:
- `as_dict` method of `Structure`: I did some preliminary profiling, and looks like this is owing to (unnecessarily) repeated calculation of `Lattice` properties like `lengths/angles` during serialization (and I believe proper caching could resolve this with correct cache voiding mechanism upon internal `_matrix` change). But **this would be a separate PR** so won't cover too much here.
- **Topic of this thread: slow `json.dump` to file**
```
Total time: 65.3412 s
File: show_structure_json_bottleneck.py
Function: save_to_file at line 38
Line # Hits Time Per Hit % Time Line Contents
==============================================================
38 @profile
39 def save_to_file(tmpdir):
40 1001 921.1 0.9 0.0 for i, struct in enumerate(STRUCTURES):
41 1000 32742869.8 32742.9 50.1 d = struct.as_dict()
42 1000 9134.1 9.1 0.0 path = os.path.join(tmpdir, f"struct_{i}.json")
43 2000 77023.5 38.5 0.1 with open(path, "w") as f:
44 1000 32511267.6 32511.3 49.8 json.dump(d, f)
```
---
And if we replace builtin `json` with the faster [drop-in replacement `orjson`](https://github.com/ijl/orjson) (I'm not aware of any caveat so far), we got (tested on WSL2 Ubuntu 22.04):
- almost 10x speed up converting Structure dict to string
- more than 10x speed up for saving Structure dict to file
- a little speed up loading JSON back
```
== Dump to String ==
json: 0.9821 s
orjson: 0.1173 s
== Save to File ==
json_plain: 3.2918 s, 105549.10 KB
orjson_plain: 0.2284 s, 97192.09 KB
json_gz: 8.0444 s, 32786.10 KB
orjson_gz: 3.3784 s, 32217.06 KB
== Load from File ==
json_plain: 3.1989 s
orjson_plain: 2.6992 s
json_gz: 3.4490 s
orjson_gz: 2.9699 s
```
Script: [compare_structure_json.txt](https://github.com/user-attachments/files/19998603/compare_structure_json.txt)
As the speed up is quite obvious with almost no code change (also considering JSON is heavily used around the code base, so not only `Structure` would benefit from this), perhaps we could consider adding `orjson` as the default JSON handler? (I'm afraid adding it as an optional dependency would incur too much code change to detect whether `orjson` is available)
But also I'm a bit unsure about the migration process as [current `json.dumps` takes `kwargs`](https://docs.python.org/3/library/json.html#basic-usage) (e.g. #4295) and this might cause breakage: https://github.com/materialsproject/pymatgen/blob/1e36ee511a89d828daa2882353c74167947a1d8e/src/pymatgen/core/structure.py#L2963
### `monty` JSON and lack of custom JSON encoder support
[`orjson` is already an optional dependency for `monty`](https://github.com/materialsvirtuallab/monty/blob/b4b879bc0b607edf05ea9180e7593428f1429385/pyproject.toml#L32) so maybe it's also good for us to double check if `orjson` is fully used from `monty`'s side as quite some JSON-related workloads are implicitly handled by `monty`.
Also as `orjson` doesn't directly support custom encoder/decoder (e.g. `MontyEncoder`), some more thoughts might be needed to this end
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`UFloat` Warnings with `ComputedEntry.energy`
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[] | 2025-05-02T18:11:41
| 2025-05-02T18:12:09
|
CONTRIBUTOR
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### Python version
Python 3.10-3.12
### Pymatgen version
2025.5.2
### Operating system version
_No response_
### Current behavior
From running our `doped` tests with the latest `pymatgen` release, we get many of these warnings (which breaks tests):
```
Using UFloat objects with std_dev==0 may give unexpected results.
```
From setting the warning level to error, the traceback shows:
```
File ~/Packages/doped/doped/core.py:1428, in DefectEntry.sc_entry_energy(self)
1425 return self._sc_entry_energy
1427 self._sc_entry_hash = hash(self.sc_entry)
-> 1428 self._sc_entry_energy = self.sc_entry.energy
1430 return self._sc_entry_energy
File ~/Packages/pymatgen/src/pymatgen/entries/computed_entries.py:350, in ComputedEntry.energy(self)
347 @property
348 def energy(self) -> float:
349 """The *corrected* energy of the entry."""
--> 350 return self.uncorrected_energy + self.correction
File ~/Packages/pymatgen/src/pymatgen/entries/computed_entries.py:367, in ComputedEntry.correction(self)
362 """
363 Returns:
364 float: the total energy correction / adjustment applied to the entry in eV.
365 """
366 # adds to ufloat(0.0, 0.0) to ensure that no corrections still result in ufloat object
--> 367 corr = ufloat(0.0, 0.0) + sum(ufloat(ea.value, ea.uncertainty) for ea in self.energy_adjustments)
368 return corr.nominal_value
File ~/miniconda3/lib/python3.10/site-packages/uncertainties/core.py:1024, in ufloat(nominal_value, std_dev, tag)
992 """
993 Create an uncertainties Variable
994
(...)
1021 and `tag` which match the input values.
1022 """
1023 if std_dev == 0:
-> 1024 warn("Using UFloat objects with std_dev==0 may give unexpected results.")
1025 return Variable(nominal_value, std_dev, tag=tag)
UserWarning: Using UFloat objects with std_dev==0 may give unexpected results.
```
I think this is due to a recent update in `uncertainties`, which now includes this warning, which occurs when there are no energy adjustments with uncertainties in `ComputedEntry.energy_adjustments`. Some discussion here: https://github.com/lmfit/lmfit-py/issues/999
### Expected Behavior
Not totally sure, but it seems like that warning is there to stay, so might make sense to catch/handle it here somehow?
Only use `ufloat` when there is an energy adjustment with non-zero uncertainty, otherwise just normal floats for correction values?
### Minimal example
```Python
from pymatgen.entries.computed_entries import ComputedEntry
ce = ComputedEntry(composition="H", energy=1)
ce.energy # shows warning
```
### Relevant files to reproduce this bug
_No response_
|
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