TY - JOUR

T1 - Predicting thermal properties of crystals using machine learning

AU - Tawfik, Sherif Abdulkader

AU - Isayev, Olexandr

AU - Spencer, Michelle J.S.

AU - Winkler, David A.

PY - 2020/2/1

Y1 - 2020/2/1

N2 - 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.

AB - 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.

KW - crystal properties

KW - density-functional theory

KW - dielectric constant

KW - entropy

KW - machine learning

UR - http://www.scopus.com/inward/record.url?scp=85079569887&partnerID=8YFLogxK

U2 - 10.1002/adts.201900208

DO - 10.1002/adts.201900208

M3 - Article

AN - SCOPUS:85079569887

VL - 3

JO - Advanced Theory and Simulations

JF - Advanced Theory and Simulations

SN - 2513-0390

IS - 2

M1 - 1900208

ER -