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license: cc-by-4.0
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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