Classifying soft self-assembled materials via unsupervised machine learning of defects

TL;DR

This paper introduces a data-driven, unsupervised machine learning workflow using SOAP data to classify soft self-assembled materials based on their defect structures and dynamic local environments.

Contribution

It presents a novel 'defectometer' method that objectively compares complex supramolecular assemblies through unsupervised clustering of molecular dynamics data.

Findings

Successfully classifies various soft supramolecular materials.

Provides a robust SOAP-based metric for structural comparison.

Enables objective analysis of defect dynamics in soft materials.

Abstract

Unlike molecular crystals, soft self-assembled fibres, micelles, vesicles, etc., exhibit a certain order in the arrangement of their constitutive monomers, but also high structural dynamicity and variability. Defects and disordered local domains that continuously form-and-repair in their structures impart to such materials unique adaptive and dynamical properties, which make them, e.g., capable to communicate with each other. However, objective criteria to compare such complex dynamical features and to classify soft supramolecular materials are non-trivial to attain. Here we show a data-driven workflow allowing us to achieve this goal. Building on unsupervised clustering of Smooth Overlap of Atomic Position (SOAP) data obtained from equilibrium molecular dynamics simulations, we can compare a variety of soft supramolecular assemblies via a robust SOAP metric. This provides us with a data-driven "defectometer" to classify different types of supramolecular materials based on the structural dynamics of the ordered/disordered local molecular environments that statistically emerge within them.