Inherent structural descriptors via machine learning

TL;DR

This paper introduces a machine learning method that identifies key collective variables based on inherent structures, improving the analysis of complex systems like nanoclusters and aiding in understanding their free-energy landscapes and phase transitions.

Contribution

The paper presents a novel machine learning approach that leverages inherent structures to find physically relevant collective variables for complex systems.

Findings

Effective at computing free-energy landscapes

Able to characterize transition rates

Describes non-equilibrium melting and freezing

Abstract

Finding proper collective variables for complex systems and processes is one of the most challenging tasks in simulations, which limits the interpretation of experimental and simulated data and the application of enhanced sampling techniques. Here, we propose a machine learning approach able to distill few, physically relevant variables by associating instantaneous configurations of the system to their corresponding inherent structures as defined in liquids theory. We apply this approach to the challenging case of structural transitions in nanoclusters, managing to characterize and explore the structural complexity of an experimentally relevant system constituted by 147 gold atoms. Our inherent-structure variables are shown to be effective at computing complex free-energy landscapes, transition rates, and at describing non-equilibrium melting and freezing processes. The effectiveness of this machine learning strategy guided by the generally-applicable concept of inherent structures shows promise to devise collective variables for a vast range of systems, including liquids, glasses, and proteins.