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
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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.
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.
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- arXiv ID : arXiv:2008.02572