Predicting aggregate morphology of sequence-defined macromolecules with recurrent neural networks

In this talk, I will present the phase separation behavior of different sequences of a coarse-grained model for sequence defined macromolecules. They exhibit a surprisingly rich phase behavior, and not only conventional liquid-liquid phase separation is observed, but also reentrant phase behavior, in which the liquid phase density decreases at lower temperatures. Most sequences form open phases consisting of aggregates, rather than a normal dense liquid. These aggregates had overall lower densities than the conventional liquid phases and complex geometries with large interconnected string-like or membrane-like clusters. Minor alterations in the sequence may lead to large changes in the overall phase behavior, a fact of significant potential relevance for biology and for designing self-assembled structures using block copolymers. I will discuss recent results from unsupervised manifold learning (UMAP) to classify the different aggregate types and what we can learn from machine learning. Using a bidirectional-Gated Recurrent Units-based Neural Network (RNN), we can now predict which sequence will self-assemble into what aggregate structure.