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arXiv · 1905.01536

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

Jutta Rogal·Elia Schneider·Mark E. Tuckerman
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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.

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Jutta Rogal, Elia Schneider, Mark E. Tuckerman. 2019-05-04. Neural network based path collective variables for enhanced sampling of phase transformations. https://doi.org/10.1103/physrevlett.123.245701

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