Model for formation_energy_peratom¶
- Description: This is a benchmark to evaluate how accurately an AI model can predict the superconducting transition temperature of 3D materials. Ref: https://www.nature.com/articles/s41524-022-00933-1.
Reference(s): https://www.nature.com/articles/s41524-022-00933-1, https://doi.org/10.1103/PhysRevMaterials.2.083801, https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.145301, https://www.nature.com/articles/s41524-021-00650-1, https://github.com/atomgptlab/alignn
Model benchmarks
| Model name | Dataset | MAE | Team name | Dataset size | Date submitted | Notes |
|---|---|---|---|---|---|---|
| cfid | dft_3d | 1.9989 | JARVIS | 616 | 01-14-2023 | CSV, JSON, run.sh, Info |
| alignn_model | dft_3d | 2.0316 | ALIGNN | 616 | 01-14-2023 | CSV, JSON, run.sh, Info |
| alignn_2026_knn | dft_3d | 1.4904 | AtomGPTLab | 616 | 07-21-2026 | CSV, JSON, run.sh, Info |
| cgcnn_model | dft_3d | 2.5743 | CGCNN | 616 | 01-14-2023 | CSV, JSON, run.sh, Info |
| alignn_2026_radius | dft_3d | 1.6365 | AtomGPTLab | 616 | 07-07-2026 | CSV, JSON, run.sh, Info |