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Model for n-Seebeck

  • Description: This is a benchmark to evaluate how accurately an AI model can predict the n-doped Seebeck Coefficient 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 accuracy.


Model benchmarks

Model nameDataset MAE Team name Dataset size Date submitted Notes
kgcnn_cgcnndft_3d45.6596kgcnn2321009-26-2023CSV, JSON,, Info
kgcnn_coNGNdft_3d40.0977kgcnn2321005-06-2023CSV, JSON,, Info
alignn_modeldft_3d40.9214ALIGNN2321001-14-2023CSV, JSON,, Info
matminer_xgboostdft_3d44.2229UofT2321005-22-2023CSV, JSON,, Info
kgcnn_megnetdft_3d47.2813kgcnn2321005-06-2023CSV, JSON,, Info
cgcnn_modeldft_3d49.3172CGCNN2321001-14-2023CSV, JSON,, Info
matminer_rfdft_3d43.9839UofT2321005-22-2023CSV, JSON,, Info
kgcnn_coGNdft_3d39.2692kgcnn2321005-06-2023CSV, JSON,, Info
kgcnn_dimenetPPdft_3d54.2759kgcnn2321005-06-2023CSV, JSON,, Info
kgcnn_schnetdft_3d47.244kgcnn2321009-26-2023CSV, JSON,, Info