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Sequence control in synthetic copolymers remains a tantalizing objective in polymer science due to the influence of sequence on material properties and self-organization. A greater understanding of sequence development throughout the polymerization process will aid the design of simple, generalizable methods to control sequence and tune supramolecular assembly. In previous simulations of solution-based step-growth copolymerizations, we have shown that weak, non-bonding attractions between monomers of the same type can produce a microphase separation among the lengthening nascent oligomers and thereby alter sequence. This work explores the phenomenon further, examining how effective attractive interactions, mediated by a solvent selective for one of the reacting species, impact the development of sequence and the supramolecular assembly in a simple A–B copolymerization. We find that as the effective attractions between monomers increase, an emergent self-organization of the reactants causes a shift in reaction kinetics and sequence development. When the solvent-mediated interactions are selective enough, the simple mixture of A and B monomers oligomerize and self-assemble into structures characteristic of amphiphilic copolymers. The composition and morphology of these structures and the sequences of their chains are sensitive to the relative balance of affinities between the comonomer species. Our results demonstrate the impact of differing A–B monomer–solvent affinities on sequence development in solution-based copolymerizations and are of consequence to the informed design of synthetic methods for sequence controlled amphiphilic copolymers and their aggregates.
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This content will become publicly available on February 17, 2026
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[thin space (1/6-em)]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.
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- PAR ID:
- 10580312
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- Journal Name:
- Soft Matter
- Volume:
- 21
- Issue:
- 11
- ISSN:
- 1744-683X
- Page Range / eLocation ID:
- 2143 to 2151
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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