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Keyword: local spin density approximation ×Clear all

2 claims from 1 paper

1. Materials Science › Machine Learning in Materials Science

   How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

   Schütt, Glawe, Brockherde, Sanna, Müller and Gross · Physical Review B · 2014

   The authors propose using machine learning, trained on density-functional calculations, to quickly predict solid-state electronic properties, and introduce a new crystal structure representation for this.

   Unchecked2 claims
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   1. UncheckedCommon ways of encoding atoms as input data, such as the Coulomb matrix, are reported to work poorly for training machine learning on periodic solids.“We find that conventional representations of the input data, such as the Coulomb matrix, are not suitable for the training of learning machines in the case of periodic solids.”
   2. Unchecked“Due to magnetic phenomena learning on d systems is found more difficult than in pure sp systems.”