Massively parallelization strategy for material simulation using high-dimensional neural network potential.

Abstract

The potential energy surface (PES) calculation is the bottleneck for modern material simulation. The high-dimensional neural network (HDNN) technique emerged recently appears to be a problem solver for fast and accurate PES computation. The major cost of the HDNN lies at the computation of the structural descriptors that capture the geometrical environment of atoms. Here, we introduce a massive parallelization strategy optimized for our recently developed power-type structural descriptor. The method involves three-levels: from the top to the bottom the parallelization is over atoms first, then, over structural descriptors and finally over the n-body functions. We illustrate the parallelization method in a boron crystal system and show that the parallelization efficiency is maximally 100%, 58%, and 34% at each level. © 2018 Wiley Periodicals, Inc.