PNNL

Abstract

Neural network potentials (NNPs) can greatly accelerate atomistic simulations relative to \textit{ab initio} methods, allowing one to sample a broader range of structural outcomes and transformation pathways. In this work, we demonstrate an active sampling algorithm that trains an NNP able to produce microstructural evolutions with accuracy comparable to those obtained by density functional theory (DFT), exemplified during structure optimizations for a model Cu-Ni multilayer system. We then use the NNP in conjunction with a perturbation scheme to stochastically sample structural and energetic changes caused by shear-induced deformation, demonstrating the range of possible intermixing and vacancy migration pathways that can be obtained as a result of the speedups provided by the NNP. The code to implement our active learning strategy and NNP-driven stochastic shear simulations is openly available at \url{https://github.com/pnnl/Active-Sampling-for-Atomistic-Potentials}.