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Model for Cu FF energy

  • Description: This is an AI benchmark to evaluate how accurately a machine learning force-field (MLFF) can predict the total energy of Cu using the relaxation trajectories (energy and forces of intermediate steps) of the mlearn dataset, calculated with the PBE density functional. The dataset contains different types of chemical formula and atomic structures. Here we use mean absolute error (MAE) to compare MLFFs with respect to DFT (PBE) accuracy. External links: https://github.com/materialsvirtuallab/mlearn


Reference(s): https://github.com/materialsvirtuallab/maml, https://github.com/materialsvirtuallab/m3gnet, https://github.com/CederGroupHub/chgnet, https://doi.org/10.1021/acs.jpca.9b08723, https://www.nature.com/articles/s43588-022-00349-3, https://pubs.rsc.org/en/content/articlehtml/2023/dd/d2dd00096b

Model benchmarks

Model nameDataset Accuracy Team name Dataset size Date submitted Notes
chgnet_pretrainedmlearn_Cu2.9263CHGNET29308-07-2023CSV, JSON, run.sh, Info
m3gnet_pretrainedmlearn_Cu1.1195M3GNET29301-14-2023CSV, JSON, run.sh, Info
alignnff_mlearn_wt1mlearn_Cu1.5529JARVIS29301-14-2023CSV, JSON, run.sh, Info
matgl_mlearnmlearn_Cu0.8696Matgl29301-14-2023CSV, JSON, run.sh, Info
alignnff_mlearn_all_wt1mlearn_Cu2.7425JARVIS29301-14-2023CSV, JSON, run.sh, Info
snap_mlearnmlearn_Cu1.4912JARVIS29301-14-2023CSV, JSON, run.sh, Info
matgl_pretrainedmlearn_Cu3.6418Matgl29301-14-2023CSV, JSON, run.sh, Info