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

Autor: Rogal, Jutta, Schneider, Elia, Tuckerman, Mark E.
Rok vydání: 2019
Předmět:
Zdroj: Phys. Rev. Lett. 123, 245701 (2019)
Druh dokumentu: Working Paper
DOI: 10.1103/PhysRevLett.123.245701
Popis: 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.
Databáze: arXiv