Mlearn -- ColabFit
Collection
Datasets from mlearn -- Zuo et al., JPCA 2020 (https://doi.org/10.1021/acs.jpca.9b08723) in ColabFit format
•
12 items
•
Updated
property_id
string | property_hash
string | last_modified
timestamp[us] | dataset_id
string | multiplicity
int32 | software
string | method
string | energy
float64 | atomic_forces
list | cauchy_stress
list | cauchy_stress_volume_normalized
bool | electronic_band_gap
float64 | electronic_band_gap_type
string | formation_energy
float64 | adsorption_energy
float64 | atomization_energy
float64 | max_force_norm
float64 | mean_force_norm
float64 | energy_above_hull
float64 | configuration_id
string | configuration_hash
string | structure_hash
string | cell
list | positions
list | pbc
list | chemical_formula_hill
string | chemical_formula_reduced
string | chemical_formula_anonymous
string | elements
list | elements_ratios
list | atomic_numbers
list | nsites
int32 | nelements
int32 | nperiodic_dimensions
int32 | dimension_types
list | names
list | labels
list | property_metadata_path
string | configuration_metadata_path
string | hash
string | configuration_metadata
string | property_metadata
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PO_5185879287037747380405520
|
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| 2025-04-16T16:54:34
|
DS_jf20mvwao5xi_0
| 1
|
VASP
|
DFT-PBE
| -3.797287
|
[
[
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[
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| null | null | null | null | null | 0
| 0
| null |
CO_1224220972052828166370387
|
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|
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|
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[
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true,
true
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Li2
|
Li
|
A
|
[
"Li"
] |
[
1
] |
[
3,
3
] | 2
| 1
| 3
|
[
1,
1,
1
] |
[
"test_Li_1x1x1_cell_mode_4_strain_0.01"
] | null |
data/MD/3085/MD_3675108599051427861473085.json
| null | null | null |
{"input": {"energy-convergence": {"value": 1e-05, "units": "eV"}, "forces-convergence": {"value": 0.02, "units": "eV/Ang"}, "k-point-mesh": "3x3x3", "kinetic-energy-cutoff": {"value": 520, "units": "eV"}}, "hash": "3675108599051427861473085714243225308802435611563687933878560098495337117258463412585021990865015101366300445184453674582578239621276593682205519717051581", "id": "MD_3675108599051427861473085"}
|
PO_5511073803865292096145893
|
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| 2025-04-16T15:44:22
|
DS_jf20mvwao5xi_0
| 1
|
VASP
|
DFT-PBE
| -100.746891
|
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| null | null | null | null | null | 0.629306
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Li54
|
Li
|
A
|
[
"Li"
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[
1
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[
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] | 54
| 1
| 3
|
[
1,
1,
1
] |
[
"test_Snapshot_82500_of_100000_of_AIMD_NVT_simulation_for_normal_structure_at_300_K"
] | null |
data/MD/3085/MD_3675108599051427861473085.json
| null | null | null |
{"input": {"energy-convergence": {"value": 1e-05, "units": "eV"}, "forces-convergence": {"value": 0.02, "units": "eV/Ang"}, "k-point-mesh": "3x3x3", "kinetic-energy-cutoff": {"value": 520, "units": "eV"}}, "hash": "3675108599051427861473085714243225308802435611563687933878560098495337117258463412585021990865015101366300445184453674582578239621276593682205519717051581", "id": "MD_3675108599051427861473085"}
|
PO_5604460030687065417966912
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| 2025-04-16T20:11:56
|
DS_jf20mvwao5xi_0
| 1
|
VASP
|
DFT-PBE
| -94.587083
|
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DS_jf20mvwao5xi_0
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| 2025-04-16T15:09:30
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DS_jf20mvwao5xi_0
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data/MD/3085/MD_3675108599051427861473085.json
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data/MD/3085/MD_3675108599051427861473085.json
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{"input": {"energy-convergence": {"value": 1e-05, "units": "eV"}, "forces-convergence": {"value": 0.02, "units": "eV/Ang"}, "k-point-mesh": "3x3x3", "kinetic-energy-cutoff": {"value": 520, "units": "eV"}}, "hash": "3675108599051427861473085714243225308802435611563687933878560098495337117258463412585021990865015101366300445184453674582578239621276593682205519717051581", "id": "MD_3675108599051427861473085"}
|
Cite this dataset
Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., Csányi, G., Shapeev, A. V., Thompson, A. P., Wood, M. A., and Ong, S. P. mlearn Li test. ColabFit, 2023. https://doi.org/10.60732/d8a6d50c
Cite this dataset
Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., Csányi, G., Shapeev, A. V., Thompson, A. P., Wood, M. A., and Ong, S. P. mlearn Li test. ColabFit, 2023. https://doi.org/10.60732/d8a6d50cThis dataset has been curated and formatted for the ColabFit Exchange
This dataset is also available on the ColabFit Exchange:
https://materials.colabfit.org/id/DS_jf20mvwao5xi_0
Visit the ColabFit Exchange to search additional datasets by author, description, element content and more.
https://materials.colabfit.org
Dataset Name
mlearn Li test
Description
A comprehensive DFT data set was generated for six elements - Li, Mo, Ni, Cu, Si, and Ge. These elements were chosen to span a variety of chemistries (main group metal, transition metal, and semiconductor), crystal structures (bcc, fcc, and diamond) and bonding types (metallic and covalent). This dataset comprises only the Li configurations
Dataset authors
Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell A. Wood, Shyue Ping Ong
Publication
https://doi.org/10.1021/acs.jpca.9b08723
Original data link
https://github.com/materialsvirtuallab/mlearn
License
BSD-3-Clause
Number of unique molecular configurations
29
Number of atoms
1320
Elements included
Li
Properties included
energy, atomic forces, cauchy stress
Usage
ds.parquet: Aggregated dataset information.co/directory: Configuration rows each include a structure, calculated properties, and metadata.cs/directory : Configuration sets are subsets of configurations grouped by some common characteristic. Ifcs/does not exist, no configurations sets have been defined for this dataset.cs_co_map/directory : The mapping of configurations to configuration sets (if defined).
ColabFit Exchange documentation includes descriptions of content and example code for parsing parquet files:
- Downloads last month
- 27
Size of downloaded dataset files:
128 kB
Size of the auto-converted Parquet files:
128 kB
Number of rows:
63