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Model for Si FF energy

  • Description: This is an AI benchmark to evaluate how accurately a machine learning force-field (MLFF) can predict the total energy of Si 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:


Model benchmarks

Model nameDataset Accuracy Team name Dataset size Date submitted Notes
nequip_mlearnmlearn_Si83.841MIR23907-22-2023CSV, JSON,, Info
allegro_mlearnmlearn_Si37.8701MIR23907-22-2023CSV, JSON,, Info
chgnet_pretrainedmlearn_Si3.5833CHGNET23908-07-2023CSV, JSON,, Info
snap_mlearnmlearn_Si1.1141JARVIS23901-14-2023CSV, JSON,, Info
matgl_pretrainedmlearn_Si5.8323Matgl23901-14-2023CSV, JSON,, Info
m3gnet_pretrainedmlearn_Si6.9913M3GNET23901-14-2023CSV, JSON,, Info
chgnet_mlearnmlearn_Si1.7295CHGNET23901-29-2024CSV, JSON,, Info
alignnff_mlearn_wt1mlearn_Si1.1766JARVIS23901-14-2023CSV, JSON,, Info
matgl_mlearnmlearn_Si0.8759Matgl23901-14-2023CSV, JSON,, Info
alignnff_mlearn_all_wt1mlearn_Si3.003JARVIS23901-14-2023CSV, JSON,, Info