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

Rajkumar, Aravinthen; Brommer, P.E.; Figiel, Łukasz

doi:10.1039/d2sm01516a

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2024

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Cited by 2 publications

References 48 publications

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

Garcia Rodolfo,

Tchoufag,

Goujon

et al. 2024

Macromolecules

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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.

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

Garcia Rodolfo,

Tchoufag,

Goujon

et al. 2024

Macromolecules

View full text Add to dashboard Cite

show abstract

Bayesian Multi-Scale Modelling of Hydrogen Permeation in Fibre-Reinforced Polymer Composites

Angus,

Rajkumar,

Figiel

2024

Preprint

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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...

Cited by 2 publications

References 48 publications

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

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

Bayesian Multi-Scale Modelling of Hydrogen Permeation in Fibre-Reinforced Polymer Composites

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