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Vibrational Properties of Metastable Polymorph Structures by Machine Learning.

Publication ,  Journal Article
Legrain, F; van Roekeghem, A; Curtarolo, S; Carrete, J; Madsen, GKH; Mingo, N
Published in: Journal of chemical information and modeling
December 2018

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.

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Published In

Journal of chemical information and modeling

DOI

EISSN

1549-960X

ISSN

1549-9596

Publication Date

December 2018

Volume

58

Issue

12

Start / End Page

2460 / 2466

Related Subject Headings

  • Vibration
  • Molecular Structure
  • Models, Chemical
  • Medicinal & Biomolecular Chemistry
  • Materials Testing
  • Machine Learning
  • 3407 Theoretical and computational chemistry
  • 3404 Medicinal and biomolecular chemistry
  • 0802 Computation Theory and Mathematics
  • 0307 Theoretical and Computational Chemistry
 

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Legrain, F., van Roekeghem, A., Curtarolo, S., Carrete, J., Madsen, G. K. H., & Mingo, N. (2018). Vibrational Properties of Metastable Polymorph Structures by Machine Learning. Journal of Chemical Information and Modeling, 58(12), 2460–2466. https://doi.org/10.1021/acs.jcim.8b00279
Legrain, Fleur, Ambroise van Roekeghem, Stefano Curtarolo, Jesús Carrete, Georg K. H. Madsen, and Natalio Mingo. “Vibrational Properties of Metastable Polymorph Structures by Machine Learning.Journal of Chemical Information and Modeling 58, no. 12 (December 2018): 2460–66. https://doi.org/10.1021/acs.jcim.8b00279.
Legrain F, van Roekeghem A, Curtarolo S, Carrete J, Madsen GKH, Mingo N. Vibrational Properties of Metastable Polymorph Structures by Machine Learning. Journal of chemical information and modeling. 2018 Dec;58(12):2460–6.
Legrain, Fleur, et al. “Vibrational Properties of Metastable Polymorph Structures by Machine Learning.Journal of Chemical Information and Modeling, vol. 58, no. 12, Dec. 2018, pp. 2460–66. Epmc, doi:10.1021/acs.jcim.8b00279.
Legrain F, van Roekeghem A, Curtarolo S, Carrete J, Madsen GKH, Mingo N. Vibrational Properties of Metastable Polymorph Structures by Machine Learning. Journal of chemical information and modeling. 2018 Dec;58(12):2460–2466.
Journal cover image

Published In

Journal of chemical information and modeling

DOI

EISSN

1549-960X

ISSN

1549-9596

Publication Date

December 2018

Volume

58

Issue

12

Start / End Page

2460 / 2466

Related Subject Headings

  • Vibration
  • Molecular Structure
  • Models, Chemical
  • Medicinal & Biomolecular Chemistry
  • Materials Testing
  • Machine Learning
  • 3407 Theoretical and computational chemistry
  • 3404 Medicinal and biomolecular chemistry
  • 0802 Computation Theory and Mathematics
  • 0307 Theoretical and Computational Chemistry