Predicting thermal properties of crystals using machine learning

Sherif Abdulkader Tawfik, Olexandr Isayev, Michelle J.S. Spencer, David A. Winkler

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)

Abstract

Calculating vibrational properties of crystals using quantum mechanical (QM) methods is a challenging problem in computational material science. This problem is solved using complementary machine learning methods that rapidly and reliably recapitulate entropy, specific heat, effective polycrystalline dielectric function, and a non-vibrational property (band gap) for materials calculated by accurate but lengthy QM methods. The materials are described mathematically using property-labeled materials fragment descriptors. The machine learning models predict the QM properties with root mean square errors of 0.31 meV per atom per K for entropy, 0.18 meV per atom per K for specific heat, 4.41 for the trace of the dielectric tensor, and 0.5 eV for band gap. These models are sufficiently accurate to allow rapid screening of large numbers of crystal structures to accelerate material discovery.

Original languageEnglish
Article number1900208
Number of pages6
JournalAdvanced Theory and Simulations
Volume3
Issue number2
DOIs
Publication statusPublished - 1 Feb 2020

Keywords

  • crystal properties
  • density-functional theory
  • dielectric constant
  • entropy
  • machine learning

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