Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Bohayra Mortazavi
  • Ivan S. Novikov
  • Evgeny V. Podryabinkin
  • Stephan Roche
  • Timon Rabczuk
  • Alexander V. Shapeev
  • Xiaoying Zhuang

Externe Organisationen

  • Universität Stuttgart
  • Institució Catalana de Recerca i Estudis Avançats (ICREA)
  • Tongji University
  • Skolkovo Innovation Center
  • Universidad Autónoma de Barcelona (UAB)
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Aufsatznummer100685
FachzeitschriftApplied Materials Today
Jahrgang20
Frühes Online-Datum13 Mai 2020
PublikationsstatusVeröffentlicht - Sept. 2020

Abstract

Phononic properties are commonly studied by calculating force constants using the density functional theory (DFT) simulations. Although DFT simulations offer accurate estimations of phonon dispersion relations or thermal properties, but for low-symmetry and nanoporous structures the computational cost quickly becomes very demanding. Moreover, the computational setups may yield nonphysical imaginary frequencies in the phonon dispersion curves, impeding the assessment of phononic properties and the dynamical stability of the considered system. Here, we compute phonon dispersion relations and examine the dynamical stability of a large ensemble of novel materials and compositions. We propose a fast and convenient alternative to DFT simulations which derived from machine-learning interatomic potentials passively trained over computationally efficient ab-initio molecular dynamics trajectories. Our results for diverse two-dimensional (2D) nanomaterials confirm that the proposed computational strategy can reproduce fundamental thermal properties in close agreement with those obtained via the DFT approach. The presented method offers a stable, efficient, and convenient solution for the examination of dynamical stability and exploring the phononic properties of low-symmetry and porous 2D materials.

ASJC Scopus Sachgebiete

Zitieren

Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials. / Mortazavi, Bohayra; Novikov, Ivan S.; Podryabinkin, Evgeny V. et al.
in: Applied Materials Today, Jahrgang 20, 100685, 09.2020.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Mortazavi, B, Novikov, IS, Podryabinkin, EV, Roche, S, Rabczuk, T, Shapeev, AV & Zhuang, X 2020, 'Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials', Applied Materials Today, Jg. 20, 100685. https://doi.org/10.48550/arXiv.2005.04913, https://doi.org/10.1016/j.apmt.2020.100685
Mortazavi, B., Novikov, I. S., Podryabinkin, E. V., Roche, S., Rabczuk, T., Shapeev, A. V., & Zhuang, X. (2020). Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials. Applied Materials Today, 20, Artikel 100685. https://doi.org/10.48550/arXiv.2005.04913, https://doi.org/10.1016/j.apmt.2020.100685
Mortazavi B, Novikov IS, Podryabinkin EV, Roche S, Rabczuk T, Shapeev AV et al. Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials. Applied Materials Today. 2020 Sep;20:100685. Epub 2020 Mai 13. doi: 10.48550/arXiv.2005.04913, 10.1016/j.apmt.2020.100685
Mortazavi, Bohayra ; Novikov, Ivan S. ; Podryabinkin, Evgeny V. et al. / Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials. in: Applied Materials Today. 2020 ; Jahrgang 20.
Download
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abstract = "Phononic properties are commonly studied by calculating force constants using the density functional theory (DFT) simulations. Although DFT simulations offer accurate estimations of phonon dispersion relations or thermal properties, but for low-symmetry and nanoporous structures the computational cost quickly becomes very demanding. Moreover, the computational setups may yield nonphysical imaginary frequencies in the phonon dispersion curves, impeding the assessment of phononic properties and the dynamical stability of the considered system. Here, we compute phonon dispersion relations and examine the dynamical stability of a large ensemble of novel materials and compositions. We propose a fast and convenient alternative to DFT simulations which derived from machine-learning interatomic potentials passively trained over computationally efficient ab-initio molecular dynamics trajectories. Our results for diverse two-dimensional (2D) nanomaterials confirm that the proposed computational strategy can reproduce fundamental thermal properties in close agreement with those obtained via the DFT approach. The presented method offers a stable, efficient, and convenient solution for the examination of dynamical stability and exploring the phononic properties of low-symmetry and porous 2D materials.",
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AU - Mortazavi, Bohayra

AU - Novikov, Ivan S.

AU - Podryabinkin, Evgeny V.

AU - Roche, Stephan

AU - Rabczuk, Timon

AU - Shapeev, Alexander V.

AU - Zhuang, Xiaoying

N1 - Funding Information: All of the sources of funding for the work described in this publication are acknowledged below: B.M. and X.Z. appreciate the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). E.V.P, I.S.N., and A.V.S. were supported by the Russian Science Foundation (Grant No 18-13-00479). ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and funded by the CERCA Programme/Generalitat de CatalunyaB.M. and X.Z. appreciate the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). E.V.P, I.S.N. and A.V.S. were supported by the Russian Science Foundation (Grant No 18-13-00479). ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and funded by the CERCA Programme/Generalitat de Catalunya.

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N2 - Phononic properties are commonly studied by calculating force constants using the density functional theory (DFT) simulations. Although DFT simulations offer accurate estimations of phonon dispersion relations or thermal properties, but for low-symmetry and nanoporous structures the computational cost quickly becomes very demanding. Moreover, the computational setups may yield nonphysical imaginary frequencies in the phonon dispersion curves, impeding the assessment of phononic properties and the dynamical stability of the considered system. Here, we compute phonon dispersion relations and examine the dynamical stability of a large ensemble of novel materials and compositions. We propose a fast and convenient alternative to DFT simulations which derived from machine-learning interatomic potentials passively trained over computationally efficient ab-initio molecular dynamics trajectories. Our results for diverse two-dimensional (2D) nanomaterials confirm that the proposed computational strategy can reproduce fundamental thermal properties in close agreement with those obtained via the DFT approach. The presented method offers a stable, efficient, and convenient solution for the examination of dynamical stability and exploring the phononic properties of low-symmetry and porous 2D materials.

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KW - Interatomic potentials

KW - Machine-learning

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