Model for exfoliation_energy¶
- Description: This is a benchmark to evaluate how accurately an AI model can predict the energy above the exfoliation energy (isolating a monolayer from a layered structure) 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/10.1103/PhysRevMaterials.2.083801, https://www.nature.com/articles/s41524-021-00650-1, https://www.nature.com/articles/s41524-023-01012-9;https://hackingmaterials.lbl.gov/matminer, https://github.com/YKQ98/Matformer, https://hackingmaterials.lbl.gov/matminer/, https://github.com/aimat-lab/gcnn_keras, https://doi.org/10.48550/arXiv.2305.11842, https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.145301, https://www.nature.com/articles/s41524-020-00440-1
Model benchmarks
Model name | Dataset | MAE | Team name | Dataset size | Date submitted | Notes |
---|---|---|---|---|---|---|
kgcnn_cgcnn | dft_3d | 45.7621 | kgcnn | 812 | 09-26-2023 | CSV, JSON, run.sh, Info |
kgcnn_schnet | dft_3d | 48.3027 | kgcnn | 812 | 09-26-2023 | CSV, JSON, run.sh, Info |
kgcnn_coGN | dft_3d | 47.6979 | kgcnn | 812 | 05-06-2023 | CSV, JSON, run.sh, Info |
matminer_lgbm | dft_3d | 49.5104 | Matminer | 812 | 01-14-2023 | CSV, JSON, run.sh, Info |
alignn_model | dft_3d | 52.7033 | ALIGNN | 812 | 01-14-2023 | CSV, JSON, run.sh, Info |
kgcnn_megnet | dft_3d | 68.2435 | kgcnn | 812 | 05-06-2023 | CSV, JSON, run.sh, Info |
cfid_chem | dft_3d | 63.769 | JARVIS | 812 | 01-14-2023 | CSV, JSON, run.sh, Info |
matminer_rf | dft_3d | 42.7554 | UofT | 812 | 05-22-2023 | CSV, JSON, run.sh, Info |
cfid | dft_3d | 62.1169 | JARVIS | 812 | 01-14-2023 | CSV, JSON, run.sh, Info |
matminer_xgboost | dft_3d | 40.887 | UofT | 812 | 05-22-2023 | CSV, JSON, run.sh, Info |
matformer | dft_3d | 51.661 | JARVIS | 812 | 01-14-2023 | CSV, JSON, run.sh, Info |
kgcnn_dimenetPP | dft_3d | 46.1517 | kgcnn | 812 | 05-06-2023 | CSV, JSON, run.sh, Info |
cgcnn_model | dft_3d | 52.7033 | CGCNN | 812 | 01-14-2023 | CSV, JSON, run.sh, Info |
kgcnn_coNGN | dft_3d | 46.272 | kgcnn | 812 | 05-06-2023 | CSV, JSON, run.sh, Info |