MISC

本文へのリンクあり
2020年8月6日

Enabling ab initio configurational sampling of multicomponent solids with long-range interactions using neural network potentials and active learning

  • Shusuke Kasamatsu
  • ,
  • Yuichi Motoyama
  • ,
  • Kazuyoshi Yoshimi
  • ,
  • Ushio Matsumoto
  • ,
  • Akihide Kuwabara
  • ,
  • Takafumi Ogawa

We propose a scheme for ab initio configurational sampling in multicomponent
crystalline solids using Behler-Parinello type neural network potentials (NNPs)
in an unconventional way: the NNPs are trained to predict the energies of
relaxed structures from the perfect lattice with configurational disorder
instead of the usual way of training to predict energies as functions of
continuous atom coordinates. Training set bias is avoided through an active
learning scheme. This idea is demonstrated on the calculation of the
temperature dependence of the degree of A/B site inversion in MgAl$_2$O$_4$,
which is a multivalent system requiring careful handling of long-range
interactions. The present scheme may serve as an alternative to cluster
expansion for `difficult' systems, e.g., complex bulk or interface systems with
many components and sublattices that are relevant to many technological
applications today.

リンク情報
arXiv
http://arxiv.org/abs/arXiv:2008.02572
URL
http://arxiv.org/abs/2008.02572v1
URL
http://arxiv.org/pdf/2008.02572v1 本文へのリンクあり
ID情報
  • arXiv ID : arXiv:2008.02572

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