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Model for max_efg

  • Description: This is a benchmark to evaluate how accurately an AI model can predict the maximum value of electric field gradient 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://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.145301, https://www.nature.com/articles/s41524-021-00650-1, 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_3d23.4912kgcnn1186509-26-2023CSV, JSON, run.sh, Info
kgcnn_coNGNdft_3d19.5495kgcnn1186505-06-2023CSV, JSON, run.sh, Info
matminer_xgboostdft_3d19.4382UofT1186505-22-2023CSV, JSON, run.sh, Info
alignn_modeldft_3d19.1211ALIGNN1186501-14-2023CSV, JSON, run.sh, Info
kgcnn_megnetdft_3d23.0652kgcnn1186505-06-2023CSV, JSON, run.sh, Info
matminer_rfdft_3d20.7856UofT1186505-22-2023CSV, JSON, run.sh, Info
kgcnn_coGNdft_3d20.4417kgcnn1186505-06-2023CSV, JSON, run.sh, Info
cgcnn_modeldft_3d24.6695CGCNN1186501-14-2023CSV, JSON, run.sh, Info
kgcnn_dimenetPPdft_3d26.9552kgcnn1186505-06-2023CSV, JSON, run.sh, Info
kgcnn_cgcnndft_3d22.9566kgcnn1186509-26-2023CSV, JSON, run.sh, Info