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Model for n-powerfact

  • Description: This is a benchmark to evaluate how accurately an AI model can classify a material as a thermoeletric based on the n-doped power factor (computed with DFT and BoltzTrap) from the JARVIS-DFT (dft_3d) dataset. The dataset contains different types of chemical formula and atomic structures. Here we use accuracy of classification (ACC) to compare models with respect to DFT accuracy.


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

Model benchmarks

Model nameDataset ACC Team name Dataset size Date submitted Notes
matminer_xgboostdft_3d0.8186UofT2321005-22-2023CSV, JSON, run.sh, Info
alignn_modeldft_3d0.7897ALIGNN2321001-14-2023CSV, JSON, run.sh, Info
matminer_rfdft_3d0.8065UofT2321005-22-2023CSV, JSON, run.sh, Info