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Model for mepsz

  • Description: This is a benchmark to evaluate how accurately an AI model can predict the static dielectric constant (z-direction) at the meta-GGA TBmBJ level of theory using the JARVIS-DFT (dft_3d) dataset. The dataset contains different types of chemical formula and atomic structures. Here we use mean absolute error (MAE) to compare models with respect to DFT (meta-GGA TBmBJ) accuracy.


Reference(s): https://www.nature.com/articles/s41524-023-01012-9;https://hackingmaterials.lbl.gov/matminer, https://hackingmaterials.lbl.gov/matminer/, https://www.nature.com/articles/s41524-021-00650-1, https://doi.org/10.1103/PhysRevMaterials.2.083801, https://doi.org/10.48550/arXiv.2305.11842, https://www.nature.com/articles/s41524-020-00440-1, https://github.com/aimat-lab/gcnn_keras

Model benchmarks

Model nameDataset MAE Team name Dataset size Date submitted Notes
kgcnn_schnetdft_3d25.668kgcnn1680909-26-2023CSV, JSON, run.sh, Info
kgcnn_coNGNdft_3d22.842kgcnn1680905-06-2023CSV, JSON, run.sh, Info
matminer_xgboostdft_3d24.8442UofT1680905-22-2023CSV, JSON, run.sh, Info
alignn_modeldft_3d23.7313ALIGNN1680901-14-2023CSV, JSON, run.sh, Info
kgcnn_megnetdft_3d27.292kgcnn1680905-06-2023CSV, JSON, run.sh, Info
cfid_chemdft_3d30.8879JARVIS1680901-14-2023CSV, JSON, run.sh, Info
matminer_lgbmdft_3d26.2827Matminer1680901-14-2023CSV, JSON, run.sh, Info
matminer_rfdft_3d24.6651UofT1680905-22-2023CSV, JSON, run.sh, Info
kgcnn_coGNdft_3d24.1081kgcnn1680905-06-2023CSV, JSON, run.sh, Info
cfiddft_3d29.3445JARVIS1680901-14-2023CSV, JSON, run.sh, Info
kgcnn_dimenetPPdft_3d30.3644kgcnn1680905-06-2023CSV, JSON, run.sh, Info
kgcnn_cgcnndft_3d26.6292kgcnn1680909-26-2023CSV, JSON, run.sh, Info