Datasets:
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_1000022493377778770006790
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| 2025-04-17T18:48:22
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DS_cgjdk1e2txjy_0
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PWmat
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data/MD/6439/MD_7488225288330586926136439.json
| null | null | null |
{"input": {"AVE_TEMP(K)": 507.31, "DESIRED_TEMP(K)": 500.0, "METHOD(1-VV,2-NH,3-LV,4-LVPR,5-NHRP)": 2, "TEMP(K)": 320.85, "TIME(fs)": 746.0, "TIME_INTERVAL(fs)": 200.0, "TOT_TEMP(K)": 187380.0}, "property_keys": {"energy": "eTot", "forces": "Force", "stress": "Pressure Internal"}, "hash": "7488225288330586926136439886675825103889202443971491857058219960826173489524610640403962373917711288055762376380269802085798960230401392023847708292736343", "id": "MD_7488225288330586926136439"}
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PO_1000276734518170544891649
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| 2025-04-16T18:29:13
|
DS_cgjdk1e2txjy_0
| 1
|
PWmat
|
DFT-PBE
| -79,596.46225
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DS_cgjdk1e2txjy_0
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DS_cgjdk1e2txjy_0
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DS_cgjdk1e2txjy_0
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DS_cgjdk1e2txjy_0
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DS_cgjdk1e2txjy_0
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data/MD/3044/MD_8881813817367169063163044.json
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{"input": {"AVE_TEMP(K)": 1992.3, "DESIRED_TEMP(K)": 2000.0, "METHOD(1-VV,2-NH,3-LV,4-LVPR,5-NHRP)": 2, "TEMP(K)": 2187.6, "TIME(fs)": 427.0, "TIME_INTERVAL(fs)": 100.0, "TOT_TEMP(K)": 853250.0}, "property_keys": {"energy": "eTot", "forces": "Force", "stress": "Pressure Internal"}, "hash": "8881813817367169063163044000505570768338543659289864924467448804098012178742339310219791641196994620882256579237495434504554639790079241212214641962510581", "id": "MD_8881813817367169063163044"}
|
End of preview. Expand
in Data Studio
Cite this dataset
Han, T., Li, J., Liu, L., Li, F., and Wang, L. PWMLFF feature comparison NPJ2023. ColabFit, 2024. https://doi.org/10.60732/209e0c9c
Cite this dataset
Han, T., Li, J., Liu, L., Li, F., and Wang, L. PWMLFF feature comparison NPJ2023. ColabFit, 2024. https://doi.org/10.60732/209e0c9cThis 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_cgjdk1e2txjy_0
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Dataset Name
PWMLFF feature comparison NPJ2023
Description
Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
Dataset authors
Ting Han, Jie Li, Liping Liu, Fengyu Li, Lin-Wang Wang
Publication
https://www.doi.org/10.1088/1367-2630/acf2bb
Original data link
https://github.com/LonxunQuantum/PWMLFF_library/tree/main
License
CC-BY-4.0
Number of unique molecular configurations
17255
Number of atoms
918240
Elements included
C, H, Mg, Ni, O, Si
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:
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