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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.


Reference(s): https://doi.org/10.48550/arXiv.2305.11842, https://github.com/aimat-lab/gcnn_keras, https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.145301, https://www.nature.com/articles/s41524-023-01012-9;https://hackingmaterials.lbl.gov/matminer, https://www.nature.com/articles/s41524-020-00440-1, https://www.nature.com/articles/s41524-021-00650-1

Model benchmarks

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