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Model for Li FF forces

  • Description: This is an AI benchmark to evaluate how accurately a machine learning force-field (MLFF) can predict the forces of Li 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 multi-mean absolute error (multi-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 Multimae Team name Dataset size Date submitted Notes
chgnet_pretrainedmlearn_Li0.0464678409069162CHGNET27008-07-2023CSV, JSON, run.sh, Info
alignnff_pretrained_wt10mlearn_Li0.14085900317110844JARVIS27001-14-2023CSV, JSON, run.sh, Info
m3gnet_pretrainedmlearn_Li0.050938555232858874M3GNET27001-14-2023CSV, JSON, run.sh, Info
alignnff_mlearn_wt1mlearn_Li0.2639976500154132JARVIS27001-14-2023CSV, JSON, run.sh, Info
alignnff_mlearn_all_wt1mlearn_Li0.031076715707407717JARVIS27001-14-2023CSV, JSON, run.sh, Info
snap_mlearnmlearn_Li0.02733803004038507JARVIS27001-14-2023CSV, JSON, run.sh, Info
matgl_pretrainedmlearn_Li0.05162559626850466Matgl27001-14-2023CSV, JSON, run.sh, Info