Autonomously revealing hidden local structures in supercooled liquids

Emanuele Boattini,Susana Marín-Aguilar,Frank Smallenburg,Saheli Mitra,Laura Filion,Giuseppe Foffi

doi:10.1038/s41467-020-19286-8

Abstract

Few questions in condensed matter science have proven as difficult to unravel as the interplay between structure and dynamics in supercooled liquids. To explore this link, much research has been devoted to pinpointing local structures and order parameters that correlate strongly with dynamics. Here we use an unsupervised machine learning algorithm to identify structural heterogeneities in three archetypical glass formers—without using any dynamical information. In each system, the unsupervised machine learning approach autonomously designs a purely structural order parameter within a single snapshot. Comparing the structural order parameter with the dynamics, we find strong correlations with the dynamical heterogeneities. Moreover, the structural characteristics linked to slow particles disappear further away from the glass transition. Our results demonstrate the power of machine learning techniques to detect structural patterns even in disordered systems, and provide a new way forward for unraveling the structural origins of the slow dynamics of glassy materials.

Highlights

Few questions in condensed matter science have proven as difficult to unravel as the interplay between structure and dynamics in supercooled liquids
The Unsupervised machine learning (UML) method we explore here is based on an algorithm we recently developed[10] for detecting crystalline structures
The structures we identify as white at strong supercooling disappear as we move away from the glass transition —clearly showing that the UML order parameter identifies local structures that are important for the dynamical slowdown

Summary

Introduction

Few questions in condensed matter science have proven as difficult to unravel as the interplay between structure and dynamics in supercooled liquids To explore this link, much research has been devoted to pinpointing local structures and order parameters that correlate strongly with dynamics. The idea of an autonomous algorithm that picks out structural heterogeneities is a appealing one in the context of supercooled liquids In this field, the last few years have seen a frantic hunt for local structural features that can be interpreted as the underlying cause for dynamical heterogeneities. Methods that can autonomously detect purely structural heterogeneities offer an unbiased fresh look at the problem

Methods

Results

Conclusion