Neural-Network-Based Path Collective Variables for Enhanced Sampling of Phase Transformations

Jutta Rogal, Elia Schneider, Mark E. Tuckerman

Research output: Contribution to journalArticle

Abstract

The investigation of the microscopic processes underlying structural phase transformations in solids is extremely challenging for both simulation and experiment. Atomistic simulations of solid-solid phase transitions require extensive sampling of the corresponding high-dimensional and often rugged energy landscape. Here, we propose a rigorous construction of a 1D path collective variable that is used in combination with enhanced sampling techniques for efficient exploration of the transformation mechanisms. The path collective variable is defined in a space spanned by global classifiers that are derived from local structural units. A reliable identification of the local structural environments is achieved by employing a neural-network-based classification scheme. The proposed path collective variable is generally applicable and enables the investigation of both transformation mechanisms and kinetics.

Original languageEnglish (US)
Article number245701
JournalPhysical Review Letters
Volume123
Issue number24
DOIs
StatePublished - Dec 13 2019

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ASJC Scopus subject areas

  • Physics and Astronomy(all)

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