Model for epsy¶
- Description: This is a benchmark to evaluate how accurately an AI model can predict the static dielectric constant (y-direction) at the OPTB88vdW level of theory 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 (OPT) accuracy.
Reference(s): https://doi.org/10.48550/arXiv.2305.11842, https://github.com/aimat-lab/gcnn_keras, https://doi.org/10.1103/PhysRevMaterials.2.083801, https://hackingmaterials.lbl.gov/matminer/, 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 name | Dataset | MAE | Team name | Dataset size | Date submitted | Notes |
|---|---|---|---|---|---|---|
| cfid | dft_3d | 25.028 | JARVIS | 44490 | 01-14-2023 | CSV, JSON, run.sh, Info |
| cfid_chem | dft_3d | 31.2443 | JARVIS | 44490 | 01-14-2023 | CSV, JSON, run.sh, Info |
| matminer_lgbm | dft_3d | 22.9547 | Matminer | 44490 | 01-14-2023 | CSV, JSON, run.sh, Info |
| kgcnn_cgcnn | dft_3d | 21.7872 | kgcnn | 44490 | 09-26-2023 | CSV, JSON, run.sh, Info |
| kgcnn_coNGN | dft_3d | 18.5923 | kgcnn | 44490 | 05-06-2023 | CSV, JSON, run.sh, Info |
| alignn_model | dft_3d | 19.9987 | ALIGNN | 44490 | 01-14-2023 | CSV, JSON, run.sh, Info |
| matminer_xgboost | dft_3d | 21.0936 | UofT | 44490 | 05-22-2023 | CSV, JSON, run.sh, Info |
| kgcnn_megnet | dft_3d | 22.4066 | kgcnn | 44490 | 05-06-2023 | CSV, JSON, run.sh, Info |
| matminer_rf | dft_3d | 21.2962 | UofT | 44490 | 05-22-2023 | CSV, JSON, run.sh, Info |
| kgcnn_coGN | dft_3d | 19.7796 | kgcnn | 44490 | 05-06-2023 | CSV, JSON, run.sh, Info |
| kgcnn_dimenetPP | dft_3d | 34.1277 | kgcnn | 44490 | 05-06-2023 | CSV, JSON, run.sh, Info |
| kgcnn_schnet | dft_3d | 22.1843 | kgcnn | 44490 | 09-26-2023 | CSV, JSON, run.sh, Info |