colabfit/PWMLFF_feature_comparison_NPJ2023

<details><summary>Cite this dataset </summary>Han, T., Li, J., Liu, L., Li, F., and Wang, L. PWMLFF feature comparison NPJ2023. ColabFit, 2024. https://doi.org/10.60732/209e0c9c</details>

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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 <br> <hr>

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. If cs/ 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). <br>

ColabFit Exchange documentation includes descriptions of content and example code for parsing parquet files:

• —Parquet parsing: example code
• —Dataset info schema
• —Configuration schema
• —Configuration set schema
• —Configuration set to configuration mapping schema