Issue 8, 2023

An extensible density-biasing approach for molecular simulations of multicomponent block copolymers

Abstract

A node-density biased Monte Carlo methodology is proposed for the molecular structure generation of complex block copolymers. Within this methodology, the block copolymer is represented as bead-spring model. Using self-consistent field theory, a density field for all monomer species within the system is calculated. Block copolymers are generated by random walk configuration biased by the density fields. The proposed algorithm then modifies the generation process by taking the global structure of the polymer into account. It is then demonstrated that these global considerations can be built into the sampling procedure, specifically through functions that assign a permissible difference in density field value between relevant monomer species to each step of the random walk. In this way, the random walk may be naturally controlled to provide the most appropriate conformations. The overall viability of this approach has been demonstrated by using the resulting configurations in molecular dynamics simulations. This new methodology is demonstrated to be powerful enough to generate molecular configurations for a much wider variety of materials than the original approach. Two key examples of the new capabilities of the method are viable configurations for ABABA pentablock copolymers and ABC triblock terpolymers.

Graphical abstract: An extensible density-biasing approach for molecular simulations of multicomponent block copolymers

Article information

Article type
Paper
Submitted
18 Nov 2022
Accepted
25 Jan 2023
First published
27 Jan 2023
This article is Open Access
Creative Commons BY license

Soft Matter, 2023,19, 1569-1585

An extensible density-biasing approach for molecular simulations of multicomponent block copolymers

A. Rajkumar, P. Brommer and Ł. Figiel, Soft Matter, 2023, 19, 1569 DOI: 10.1039/D2SM01516A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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