Predicting self-assembly of sequence-controlled copolymers with stochastic sequence variation

Sequence-controlled copolymers can self-assemble into a wide assortment of complex architectures, with exciting applications in nanofabrication and personalized medicine. However, polymer synthesis is notoriously imprecise, and stochasticity in both chemical synthesis and self-assembly poses a significant challenge to tight control over these systems. While it is increasingly viable to design “protein-like” sequences, specifying each individual monomer in a chain, the effect of variability within those sequences has not been well studied. In this work, we performed nearly 15 000 molecular dynamics simulations of sequence-controlled copolymer aggregates with varying level of sequence stochasticity. We utilized unsupervised learning to characterize the resulting morphologies and found that sequence variation leads to relatively smooth and predictable changes in morphology compared to ensembles of identical chains. Furthermore, structural response to sequence variation was accurately modeled using supervised learning, revealing several interesting trends in how specific families of sequences break down as monomer sequences become more variable. Our work presents a way forward in understanding and controlling the effect of sequence variation in sequence-controlled copolymer systems, which can hopefully be used to design advanced copolymer systems for technological applications in the future.