Model for n-powerfact¶
- Description: This is a benchmark to evaluate how accurately an AI model can predict the n-doped Power Factor 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://www.nature.com/articles/s41524-020-00440-1, https://github.com/aimat-lab/gcnn_keras, https://www.nature.com/articles/s41524-021-00650-1, https://www.nature.com/articles/s41524-023-01012-9;https://hackingmaterials.lbl.gov/matminer
Model benchmarks
| Model name | Dataset | MAE | Team name | Dataset size | Date submitted | Notes |
|---|---|---|---|---|---|---|
| alignn_model | dft_3d | 442.2993 | ALIGNN | 23210 | 01-14-2023 | CSV, JSON, run.sh, Info |
| matminer_rf | dft_3d | 475.0085 | UofT | 23210 | 05-22-2023 | CSV, JSON, run.sh, Info |
| kgcnn_coNGN | dft_3d | 456.6118 | kgcnn | 23210 | 05-06-2023 | CSV, JSON, run.sh, Info |
| kgcnn_schnet | dft_3d | 495.4136 | kgcnn | 23210 | 09-26-2023 | CSV, JSON, run.sh, Info |
| kgcnn_coGN | dft_3d | 452.235 | kgcnn | 23210 | 05-06-2023 | CSV, JSON, run.sh, Info |
| kgcnn_cgcnn | dft_3d | 485.5895 | kgcnn | 23210 | 09-26-2023 | CSV, JSON, run.sh, Info |
| kgcnn_megnet | dft_3d | 501.3722 | kgcnn | 23210 | 05-06-2023 | CSV, JSON, run.sh, Info |
| matminer_xgboost | dft_3d | 469.6279 | UofT | 23210 | 05-22-2023 | CSV, JSON, run.sh, Info |
| kgcnn_dimenetPP | dft_3d | 568.8357 | kgcnn | 23210 | 05-06-2023 | CSV, JSON, run.sh, Info |