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CHIPS FF

The chipsff repository provides a comprehensive framework for performing materials simulations with machine learning force fields (MLFFs) with Pearson Correlation Coefficient as a metric. Simulations include structural relaxation, vacancy and surface energy calculations, interface analysis, elastic properties, phonons and thermal properties. The code supports multiple universal MLFFs and integrates with the JARVIS database and the Atomic Simulation Environment (ASE) to facilitate various materials simulations and workflows.

Reference: https://github.com/usnistgov/chipsff

NamesLinks
dft_3d_chipsff-ahttps://pages.nist.gov/jarvis_leaderboard/AI/SinglePropertyPrediction/dft_3d_chipsff_a
dft_3d_chipsff-bhttps://pages.nist.gov/jarvis_leaderboard/AI/SinglePropertyPrediction/dft_3d_chipsff_b
dft_3d_chipsff-chttps://pages.nist.gov/jarvis_leaderboard/AI/SinglePropertyPrediction/dft_3d_chipsff_c
dft_3d_chipsff-volhttps://pages.nist.gov/jarvis_leaderboard/AI/SinglePropertyPrediction/dft_3d_chipsff_vol
dft_3d_chipsff-c11https://pages.nist.gov/jarvis_leaderboard/AI/SinglePropertyPrediction/dft_3d_chipsff_c11
dft_3d_chipsff-c44https://pages.nist.gov/jarvis_leaderboard/AI/SinglePropertyPrediction/dft_3d_chipsff_c44
dft_3d_chipsff-kvhttps://pages.nist.gov/jarvis_leaderboard/AI/SinglePropertyPrediction/dft_3d_chipsff_kv