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Model for Ni FF energy

  • Description: This is an AI benchmark to evaluate how accurately a machine learning force-field (MLFF) can predict the total energy of Ni 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
m3gnet_pretrainedmlearn_Ni2.7001M3GNET29401-14-2023CSV, JSON,, Info
chgnet_pretrainedmlearn_Ni7.1812CHGNET29408-07-2023CSV, JSON,, Info
matgl_pretrainedmlearn_Ni2.9438Matgl29401-14-2023CSV, JSON,, Info
snap_mlearnmlearn_Ni1.6742JARVIS29401-14-2023CSV, JSON,, Info
alignnff_mlearn_wt1mlearn_Ni146.3505JARVIS29401-14-2023CSV, JSON,, Info
alignnff_mlearn_all_wt1mlearn_Ni1.7249JARVIS29401-14-2023CSV, JSON,, Info
mishin_eammlearn_Ni134.9626JARVIS-FF29401-14-2023CSV, JSON,, Info