Unsupervised learning of sequence-specific aggregation behavior for model copolymers

Sequence specific random block copolymers in dilute conditions exhibit a surprisingly rich phase behavior, including re-entrant phase behavior and large-scale aggregation. We apply a recently developed unsupervised machine learning scheme for local environments [Reinhart, Comput. Mater. Sci., 2021, 196, 110511] to characterize these large-scale, disordered aggregates, which has been shown to be challenging using short-ranged manually derived order parameters. The machine learning algorithm we develop is able to classify the global aggregate structure directly using descriptions of the local environments. We find that the aggregates had overall lower densities than the conventional liquid phases and complex geometries with large interconnected string-like or membrane-like clusters. The resulting characterization provides a deeper understanding of the range of possible self-assembled structures and their relationships to each other.  We demonstrate that by applying unsupervised machine learning to disordered soft matter systems insights can be gained, especially when suitable order parameters are not known.