Deep machine learning interatomic potential for liquid silica

I. A. Balyakin, S. V. Rempel, R. E. Ryltsev, and A. A. Rempel
Phys. Rev. E 102, 052125 – Published 23 November 2020

Abstract

The use of machine learning to develop neural network potentials (NNP) representing the interatomic potential energy surface allows us to achieve an optimal balance between accuracy and efficiency in computer simulation of materials. A key point in developing such potentials is the preparation of a training dataset of ab initio trajectories. Here we apply a deep potential molecular dynamics (DeePMD) approach to develop NNP for silica, which is the representative glassformer widely used as a model system for simulating network-forming liquids and glasses. We show that the use of a relatively small training dataset of high-temperature ab initio configurations is enough to fabricate NNP, which describes well both structural and dynamical properties of liquid silica. In particular, we calculate the pair correlation functions, angular distribution function, velocity autocorrelation functions, vibrational density of states, and mean-square displacement and reveal a close agreement with ab initio data. We show that NNP allows us to expand significantly the time-space scales achievable in simulations and thus calculating dynamical and transport properties with more accuracy than that for ab initio methods. We find that developed NNP allows us to describe the structure of the glassy silica with satisfactory accuracy even though no low-temperature configurations were included in the training procedure. The results obtained open up prospects for simulating structural and dynamical properties of liquids and glasses via NNP.

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  • Received 7 July 2020
  • Accepted 13 October 2020

DOI:https://doi.org/10.1103/PhysRevE.102.052125

©2020 American Physical Society

Physics Subject Headings (PhySH)

Statistical Physics & ThermodynamicsNetworksCondensed Matter, Materials & Applied Physics

Authors & Affiliations

I. A. Balyakin1,2, S. V. Rempel3,2, R. E. Ryltsev1,4,5, and A. A. Rempel1,2

  • 1Institute of Metallurgy of the Ural Branch of the Russian Academy of Sciences, 620016, Ekaterinburg, Russia
  • 2Ural Federal University, NANOTECH Centre, 620002, Ekaterinburg, Russia
  • 3Institute of Solid State Chemistry of the Ural Branch of the Russian Academy of Sciences, 620145 Ekaterinburg, Russia
  • 4Vereshchagin Institute for High Pressure Physics, Russian Academy of Sciences, 108840 Troitsk, Moscow, Russia
  • 5Ural Federal University, Engineering School of Information Technologies, Telecommunications and Control Systems, 620002, Ekaterinburg, Russia

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Issue

Vol. 102, Iss. 5 — November 2020

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