Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements.

@article{Willatt2018FeatureOF,
  title={Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements.},
  author={Michael J Willatt and F. Musil and M. Ceriotti},
  journal={Physical chemistry chemical physics : PCCP},
  year={2018},
  volume={20 47},
  pages={
          29661-29668
        }
}
Machine-learning of atomic-scale properties amounts to extracting correlations between structure, composition and the quantity that one wants to predict. Representing the input structure in a way that best reflects such correlations makes it possible to improve the accuracy of the model for a given amount of reference data. When using a description of the structures that is transparent and well-principled, optimizing the representation might reveal insights into the chemistry of the data set… Expand

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