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Model for bulk modulus (Kv) of CHIPSFF dataset

  • Description: This is a benchmark to evaluate how accurately an AI model can predict the bulk modulus (Kv) using the CHIPSFF subset of JARVIS-DFT (dft_3d) dataset. The dataset contains different types of chemical formula and atomic structures of materials common to semiconductor device components. Here we use mean absolute error (MAE) to compare models with respect to DFT accuracy.


Reference(s): https://doi.org/10.48550/arXiv.2305.11842, https://github.com/usnistgov/chipsff, https://www.nature.com/articles/s41524-020-00440-1

Model benchmarks

Model name Dataset ACC Team name Dataset size Date submitted Notes
alignn_ffdft_3d_chipsff135.9185JARVIS1043-11-2025CSV, JSON, run.sh, Info
eqV2_86M_omatdft_3d_chipsff118.2267JARVIS1043-11-2025CSV, JSON, run.sh, Info
chgnetdft_3d_chipsff85.5984JARVIS1043-11-2025CSV, JSON, run.sh, Info
matgldft_3d_chipsff82.9773JARVIS1043-11-2025CSV, JSON, run.sh, Info
matgl-directdft_3d_chipsff83.7321JARVIS1043-11-2025CSV, JSON, run.sh, Info
orb-v2dft_3d_chipsff118.2786JARVIS1043-11-2025CSV, JSON, run.sh, Info
macedft_3d_chipsff94.196JARVIS1043-11-2025CSV, JSON, run.sh, Info
eqV2_31M_omatdft_3d_chipsff114.3849JARVIS1043-11-2025CSV, JSON, run.sh, Info
mace-mpadft_3d_chipsff109.9045JARVIS1043-11-2025CSV, JSON, run.sh, Info
eqV2_86M_omat_mp_salexdft_3d_chipsff113.7747JARVIS1043-11-2025CSV, JSON, run.sh, Info
sevennetdft_3d_chipsff103.6429JARVIS1043-11-2025CSV, JSON, run.sh, Info
mattersimdft_3d_chipsff110.1779JARVIS1043-11-2025CSV, JSON, run.sh, Info
eqV2_153M_omatdft_3d_chipsff117.1085JARVIS1043-11-2025CSV, JSON, run.sh, Info
orb-d3-v2dft_3d_chipsff130.4195JARVIS1043-11-2025CSV, JSON, run.sh, Info
mace-alexandriadft_3d_chipsff77.9944JARVIS1043-11-2025CSV, JSON, run.sh, Info
eqV2_31M_omat_mp_salexdft_3d_chipsff112.3685JARVIS1043-11-2025CSV, JSON, run.sh, Info