# Neural network based path collective variables for enhanced sampling of phase transformations

## TL;DR

This paper introduces a neural network-based method to construct a 1D path collective variable for enhanced sampling of phase transformations, enabling detailed exploration of phase boundary migration in materials.

## Contribution

It presents a novel approach combining neural network classification with collective variables to improve sampling of structural phase transformations.

## Key findings

- Successful identification of local structural environments
- Effective exploration of phase boundary migration in molybdenum
- Enhanced sampling technique improves understanding of phase transformations

## 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.