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Model for dfpt_piezo_max_dielectric

  • Description: This is a benchmark to evaluate how accurately an AI model can predict the maximum piezoelectric 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://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_3d26.5276kgcnn470409-26-2023CSV, JSON, run.sh, Info
kgcnn_coNGNdft_3d25.5553kgcnn470405-06-2023CSV, JSON, run.sh, Info
matminer_xgboostdft_3d36.6913UofT470405-22-2023CSV, JSON, run.sh, Info
alignn_modeldft_3d28.1514ALIGNN470401-14-2023CSV, JSON, run.sh, Info
kgcnn_megnetdft_3d30.1911kgcnn470405-06-2023CSV, JSON, run.sh, Info
matminer_rfdft_3d36.7249UofT470405-22-2023CSV, JSON, run.sh, Info
kgcnn_coGNdft_3d30.2923kgcnn470405-06-2023CSV, JSON, run.sh, Info
cgcnn_modeldft_3d32.5589CGCNN470401-14-2023CSV, JSON, run.sh, Info
kgcnn_dimenetPPdft_3d30.3358kgcnn470405-06-2023CSV, JSON, run.sh, Info
kgcnn_cgcnndft_3d30.9607kgcnn470409-26-2023CSV, JSON, run.sh, Info