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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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