Predicting Sequence-Structure Correlations of a Copolymer using Evolutionary Computing

The correlations between the sequence of monomers in a copolymer and its three-dimensional (3D) structure is a grand challenge in polymer science and biology. The properties and functions of copolymers depend on their 3D shape that has appeared to be dictated by their monomer sequence. However, the progress towards understanding the sequence-structure-property correlations and their utilization in materials engineering are slow because it is almost impossible to characterize astronomically large number of possible sequences of a copolymer using traditional experimental and simulation methods. To address this problem, here, we combine evolutionary computing and coarse-grained molecular dynamics (CGMD) simulation and study the sequence-structure correlations of a model AB type copolymer system. The CGMD based evolutionary algorithm (EA) employs evolutionary mechanisms – elitism, selection, crossover and mutations successively to generate new polymer sequences that have superior properties. The CGMD based evolutionary algorithm (EA) screens the sequence space of a single chain copolymer efficiently and identifies wide range of single molecule structures including extremal radius of gyrations (R_gs). The data is utilized to establish new sequence-structure correlations of a copolymer. We further report correlations between monomer sequence and multimolecular assemblies and bulk phases of copolymers. The work demonstrate how monomer level sequence control can be utilized to design polymer microstructure.