  Self-assembly of dilute sequence-defined macromolecules is a complex
phenomenon in which the local arrangement of chemical moieties can lead to the
formation of long-range structure. The dependence of this structure on the
sequence necessarily implies that a mapping between the two exists, yet it has
been difficult to model so far. Predicting the aggregation behavior of these
macromolecules is challenging due to the lack of effective order parameters, a
vast design space, inherent variability, and high computational costs
associated with currently available simulation techniques. Here, we accurately
predict the morphology of aggregates self-assembled from sequence-defined
macromolecules using supervised machine learning. We find that regression
models with implicit representation learning perform significantly better than
those based on engineered features such as $k$-mer counting, and a
Recurrent-Neural-Network-based regressor performs the best out of nine model
architectures we tested. Furthermore, we demonstrate the high-throughput
screening of monomer sequences using the regression model to identify
candidates for self-assembly into selected morphologies. Our strategy is shown
to successfully identify multiple suitable sequences in every test we
performed, so we hope the insights gained here can be extended to other
increasingly complex design scenarios in the future, such as the design of
sequences under polydispersity and at varying environmental conditions.
