Sequence Engineering of Copolymers using Evolutionary Computing

TL;DR

This paper introduces an evolutionary computing approach combined with molecular dynamics simulations to efficiently explore copolymer sequences, revealing how sequence variations influence structure and enabling targeted material design.

Contribution

It presents a novel method integrating evolutionary algorithms with coarse-grained simulations to analyze sequence-structure relationships in copolymers.

Findings

Identified a wide range of copolymer structures including extremal radii of gyration.

Provided new insights into sequence-Rg correlations affecting polymer properties.

Demonstrated the potential for sequence-specific control in material design.

Abstract

The correlations between the sequence of monomers in a polymer and its three-dimensional structure is a grand challenge in polymer science and biology. The properties and functions of macromolecules 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 simulation and study the sequence-structure correlations of a model AB type copolymer system. The CGMD based evolutionary algorithm screens the sequence space of the copolymer efficiently and identifies wide range of single molecule structures including extremal radius of gyrations. The data provide new insights on the sequence-Rg correlations of the copolymer system and their impact on the structure and functionality of polymeric materials. The work highlights the opportunities of sequence specific control of macromolecular structure for designing materials with exceptional properties.