| | --- |
| | license: cc-by-4.0 |
| | --- |
| | These are ALIGNNd models trained with standard quantile loss to predict the kpoints-density for different quantiles. For each quantile top 3 (quantile loss minimal on the validation set) checkpoints are recorded. |
| |
|
| | The implementation of ALIGNNd model can be found here https://github.com/stfc/goldilocks_kpoints. For these checkpoints input features are embeddings/atom_init_with_sssp_cutoffs.json, additional features are composition, structure, lattice, and metallicity embeddings |
| | |
| | # Performance of the model trained for 0.5 quantile is: |
| | |
| | MAE: 0.069 |
| | |
| | MAPE: 0.189 |
| | |
| | MSE: 0.0097 |
| | |
| | R2 score: 0.697 |
| | |
| | Spearman_corr: 0.866 |
| |
|
| | Kendall_corr: 0.677 |
| | |
| | # Associated repositories are: |
| | |
| | https://github.com/stfc/goldilocks_kpoints |
| |
|
| | https://github.com/stfc/goldilocks |
