Vibrational Properties of Metastable Polymorph Structures by Machine Learning.

Journal Article (Journal Article)

Despite vibrational properties being critical for the ab initio prediction of finite-temperature stability as well as thermal conductivity and other transport properties of solids, their inclusion in ab initio materials repositories has been hindered by expensive computational requirements. Here we tackle the challenge, by showing that a good estimation of force constants and vibrational properties can be quickly achieved from the knowledge of atomic equilibrium positions using machine learning. A random-forest algorithm trained on 121 different mechanically stable structures of KZnF3 reaches a mean absolute error of 0.17 eV/Å2 for the interatomic force constants, and it is less expensive than training the complete force field for such compounds. The predicted force constants are then used to estimate phonon spectral features, heat capacities, vibrational entropies, and vibrational free energies, which compare well with the ab initio ones. The approach can be used for the rapid estimation of stability at finite temperatures.

Full Text

Duke Authors

Cited Authors

  • Legrain, F; van Roekeghem, A; Curtarolo, S; Carrete, J; Madsen, GKH; Mingo, N

Published Date

  • December 2018

Published In

Volume / Issue

  • 58 / 12

Start / End Page

  • 2460 - 2466

PubMed ID

  • 30351054

Electronic International Standard Serial Number (EISSN)

  • 1549-960X

International Standard Serial Number (ISSN)

  • 1549-9596

Digital Object Identifier (DOI)

  • 10.1021/acs.jcim.8b00279


  • eng