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README.md

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-license: cc-by-4.0
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+---
+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.
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+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
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+# Performance of the model trained for 0.5 quantile is:
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+MAE: 0.069
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+MAPE: 0.189
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+MSE: 0.0097
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+R2 score: 0.697
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+Spearman_corr: 0.866
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+Kendall_corr: 0.677
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+# Associated repositories are:
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+https://github.com/stfc/goldilocks_kpoints
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+https://github.com/stfc/goldilocks