Band gap prediction on Materials Project (test)
0.204MAE (eV)PotNet
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| PotNet2023.06 | 0.204 | — | — | |
| Matformer2023.06 | 0.211 | — | — | |
| Matformer2022.09 | 0.211 | — | — | |
| ALIGNN2023.06 | 0.218 | — | — | |
| ALIGNNSource=Retrained2022.09 | 0.218 | — | — | |
| GATGNN2023.06 | 0.28 | — | — | |
| GATGNNSource=Retrained2022.09 | 0.28 | — | — | |
| CGCNN2023.06 | 0.292 | — | — | |
| CGCNNSource=Retrained2022.09 | 0.292 | — | — | |
| MEGNET2023.06 | 0.307 | — | — | |
| MEGNETSource=Retrained2022.09 | 0.307 | — | — | |
| GATGNNSource=Original paper results2022.09 | 0.31 | — | — | |
| MEGNetElements=89, Training structures=367202018.12 | 0.33 | — | — | |
| MEGNETSource=Original paper results2022.09 | 0.33 | — | — | |
| SchNet2023.06 | 0.345 | — | — | |
| SchNetSource=Retrained2022.09 | 0.345 | — | — | |
| CGCNNElements=87, Training structures=164852018.12 | 0.388 | — | — | |
| CGCNNSource=Original paper results2022.09 | 0.388 | — | — | |
| CGCNN# of train data=16458, Unit=eV2017.10 | — | 0.388 | — | |
| DFT# of train data=16458, Unit=eV2017.10 | — | — | 0.6 |