Propagator-Biased Chain Generation: Accurately Reverse Mapping Microphase-Separated Block Copolymers

Abstract

We introduce the Propagator-Biased Chain Generation (PBCG) algorithm, which generates initial configurations for coarse-grained molecular dynamics simulations of block copolymers presenting microphase separation. We build on the classical self-consistent field theory (SCFT) and show how its main statistical objects, the so-called forward and backward chain propagators, can be properly utilized to bias the configuration of coarse-grained bead−spring chains. Both the local volume fractions and the spatial segment distributions predicted by SCFT are accurately reproduced by configurations yielded by the algorithm. The PBCG algorithm supports the multiscale approach by allowing simulations to start in a state that is very close to the phase-separated equilibrium, typically much harder to obtain when starting from a random initial state. We demonstrate how to apply the algorithm to generic coarse-grained systems in reduced units as well as to chemically specific models of materials such as styrene-isoprene-styrene triblock copolymers.