Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport

Abstract

We develop a neuroevolution-potential (NEP) framework for generating neural network based machine-learning potentials. They are trained using an evolutionary strategy for performing large-scale molecular dynamics (MD) simulations. A descriptor of the atomic environment is constructed based on Chebyshev and Legendre polynomials. The method is implemented in graphic processing units within the open-source GPUMD package, which can attain a computational speed over $10^7$ atom-step per second using one Nvidia Tesla V100. Furthermore, per-atom heat current is available in NEP, which paves the way for efficient and accurate MD simulations of heat transport in materials with strong phonon anharmonicity or spatial disorder, which usually cannot be accurately treated either with traditional empirical potentials or with perturbative methods.

18 pages, 10 figures, 2 tables, code and data available

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• Anisotropic and high thermal conductivity in monolayer quasi-hexagonal fullerene: A comparative study against bulk phase fullerene
• Simple machine-learned interatomic potentials for complex alloys
• Phase transitions in inorganic halide perovskites from machine learning potentials
• Limits of the phonon quasi-particle picture at the cubic-to-tetragonal phase transition in halide perovskites
• E(n)-Equivariant Cartesian Tensor Passing Potential
• Ab initio Green-Kubo simulations of heat transport in solids: Method and implementation
• Variable thermal transport in black, blue, and violet phosphorene from extensive atomistic simulations with a neuroevolution potential
• Heat transport across graphene/hexagonal-BN tilted grain boundaries from phase-field crystal model and molecular dynamics simulations
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• Machine learning based modeling of disordered elemental semiconductors: understanding the atomic structure of a-Si and a-C