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Model for ALIGNN-FF energy

  • Description: This is an AI benchmark to evaluate how accurately a machine learning force-field (MLFF) can predict the total energy using the relaxation trajectories (energy and forces of intermediate steps) of the JARVIS-DFT (dft_3d) dataset, calculated with the OPTB88vdw 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 (OPT) accuracy.


Reference(s): https://doi.org/10.1039/D2DD00096B, https://pubs.rsc.org/en/content/articlehtml/2023/dd/d2dd00096b

Model benchmarks

Model nameDataset Accuracy Team name Dataset size Date submitted Notes
alignnff_pretrained_wt1alignn_ff_db0.0509JARVIS30711101-14-2023CSV, JSON, run.sh, Info
alignnff_pretrained_wt0.5alignn_ff_db0.0443JARVIS30711101-14-2023CSV, JSON, run.sh, Info
alignnff_pretrained_wt10alignn_ff_db0.0973JARVIS30711101-14-2023CSV, JSON, run.sh, Info
alignnff_pretrained_wt0.1alignn_ff_db0.0342JARVIS30711101-14-2023CSV, JSON, run.sh, Info