Neural network based path collective variables for enhanced sampling of phase transformations
Materials Science
2022-12-09 v1 Statistical Mechanics
Computational Physics
Abstract
We propose a rigorous construction of a 1D path collective variable to sample structural phase transformations in condensed matter. The path collective variable is defined in a space spanned by global collective variables that serve as classifiers derived from local structural units. A reliable identification of local structural environments is achieved by employing a neural network based classification. The 1D path collective variable is subsequently used together with enhanced sampling techniques to explore the complex migration of a phase boundary during a solid-solid phase transformation in molybdenum.
Keywords
Cite
@article{arxiv.1905.01536,
title = {Neural network based path collective variables for enhanced sampling of phase transformations},
author = {Jutta Rogal and Elia Schneider and Mark E. Tuckerman},
journal= {arXiv preprint arXiv:1905.01536},
year = {2022}
}