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Model for p-Seebeck

  • Description: This is a benchmark to evaluate how accurately an AI model can classify a material as a thermoeletric based on the p-doped Seebeck Coefficient (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.


Model benchmarks

Model nameDataset ACC Team name Dataset size Date submitted Notes
alignn_modeldft_3d0.9259ALIGNN2321001-14-2023CSV, JSON,, Info
matminer_xgboostdft_3d0.9332UofT2321005-22-2023CSV, JSON,, Info
matminer_rfdft_3d0.9237UofT2321005-22-2023CSV, JSON,, Info