Skip to content

Model for Ni FF forces

  • Description: This is an AI benchmark to evaluate how accurately a machine learning force-field (MLFF) can predict the forces 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 multi-mean absolute error (multi-MAE) to compare MLFFs with respect to DFT (PBE) accuracy. External links:


Model benchmarks

Model nameDataset Multimae Team name Dataset size Date submitted Notes
m3gnet_pretrainedmlearn_Ni0.14802784170047634M3GNET29401-14-2023CSV, JSON,, Info
chgnet_pretrainedmlearn_Ni0.06647934973460048CHGNET29408-07-2023CSV, JSON,, Info
matgl_pretrainedmlearn_Ni0.07018436358176532Matgl29401-14-2023CSV, JSON,, Info
snap_mlearnmlearn_Ni0.06181732186425153JARVIS29401-14-2023CSV, JSON,, Info
alignnff_mlearn_wt1mlearn_Ni0.20486748573868888JARVIS29401-14-2023CSV, JSON,, Info
alignnff_mlearn_all_wt1mlearn_Ni0.07642424923483106JARVIS29401-14-2023CSV, JSON,, Info
mishin_eammlearn_Ni0.1363723913792615JARVIS-FF29401-14-2023CSV, JSON,, Info
alignnff_pretrained_wt10mlearn_Ni0.44115659863729567JARVIS29401-14-2023CSV, JSON,, Info