Dissipative particle dynamics for directed self-assembly of block copolymers

Huang, H. H.; Alexander‐Katz, Alfredo

doi:10.1063/1.5117839

Cited by 28 publications

(43 citation statements)

References 62 publications

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“…Since its development, DPD has been widely used to study the dynamics and phase behavior of various polymer systems. [83][84][85] Although many investigations are phenomenological, there have been notable attempts to establish chemical specificity, starting with the early work of Groot and Warren who demonstrated a correspondence between the Flory-Huggins χ-parameter and soft-core repulsion parameters. 86 Van Vliet et al introduced pressure-and temperature-dependent repulsion parameters by extending the Flory-Huggins mapping to examine pressure-induced phase separation in polymersolvent systems.…”

Section: Dissipative Particle Dynamicsmentioning

confidence: 99%

Chemically specific coarse‐graining of polymers: Methods and prospects

Dhamankar

Webb

2021

Journal of Polymer Science

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Coarse-grained (CG) modeling is an invaluable tool for the study of polymers and other soft matter systems due to the span of spatiotemporal scales that typify their physics and behavior. Given continuing advancements in experimental synthesis and characterization of such systems, there is ever greater need to leverage and expand CG capabilities to simulate diverse soft matter systems with chemical specificity. In this review, we discuss essential modeling techniques, bottom-up coarse-graining methodologies, and outstanding challenges for the chemically specific CG modeling of polymer-based systems. This methodologically oriented discussion is complemented by representative literature examples for polymer simulation; we also offer some advisory practical considerations that should be useful for new researchers. Given its growing importance in the modeling and polymer science community, we further highlight some recent applications of machine learning that enhance CG modeling strategies. Overall, this review provides comprehensive discussion of methods and prospects for the chemically specific coarse-graining of polymers.

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Section: Dissipative Particle Dynamicsmentioning

confidence: 99%

Chemically specific coarse‐graining of polymers: Methods and prospects

Dhamankar

Webb

2021

Journal of Polymer Science

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“…Relatively recently, DPD was applied to emulate and elucidate the chemoepitaxy and graphoepitaxy preparation of thin films [274]. The authors reparametrised the density, bond distance, inter-bead potential and noise ratio.…”

Section: Experiment-inspired and Application-orientated Papersmentioning

confidence: 99%

DPD Modelling of the Self- and Co-Assembly of Polymers and Polyelectrolytes in Aqueous Media: Impact on Polymer Science

et al. 2022

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This review article is addressed to a broad community of polymer scientists. We outline and analyse the fundamentals of the dissipative particle dynamics (DPD) simulation method from the point of view of polymer physics and review the articles on polymer systems published in approximately the last two decades, focusing on their impact on macromolecular science. Special attention is devoted to polymer and polyelectrolyte self- and co-assembly and self-organisation and to the problems connected with the implementation of explicit electrostatics in DPD numerical machinery. Critical analysis of the results of a number of successful DPD studies of complex polymer systems published recently documents the importance and suitability of this coarse-grained method for studying polymer systems.

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“…Due to the astounding number of patterns now possible to realize in synthetic copolymers, effectively leveraging the possibilities of polymer self-assembly requires a quantitative understanding of the design process. Fortunately, computational approaches are available across the many lengths and time scales relevant to this problem, including Density Functional Theory, 43 Molecular Dynamics (MD) (from atomistic to coarse-grained), [44][45][46] Monte Carlo, 47 and mean field theories. 19,48,49 Mean-field theories, in particular, have emerged as a staple approach to understanding the mechanism of self-assembly and have provided critical guidance for the experimental investigation of related phenomena.…”

Section: Introductionmentioning

confidence: 99%

Predicting aggregate morphology of sequence-defined macromolecules with Recurrent Neural Networks

Bhattacharya¹,

Kleeblatt²,

Statt³

et al. 2022

Preprint

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Self-assembly of dilute sequence-defined macromolecules is a complex phenomenon in which the local arrangement of chemical moieties leads to the formation of a long-range structure. The dependence of this structure on the sequence necessarily implies that a mapping between the two exists, yet it has been difficult to model so far. Predicting the aggregation behavior of these macromolecules is challenging due to the lack of effective order parameters, a vast design space, inherent variability, and high computational costs associated with currently available simulation techniques. Here, we accurately predict the morphology of aggregates self-assembled from sequence-defined macromolecules using supervised machine learning. We find that regression models with implicit representation learning perform significantly better than those based on engineered features such as k-mer counting, and a Recurrent-Neural-Network-based regressor performs the best. Further, we demonstrate the high-throughput screening of monomer sequences using the regression model to identify candidates for self-assembly into selected morphologies. Our strategy is shown to successfully identify multiple suitable sequences in every test we performed, so we hope the insights gained here can be extended to other increasingly complex design scenarios in the future, such as the design of sequences under polydispersity and at varying environmental conditions.

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Dissipative particle dynamics for directed self-assembly of block copolymers

Cited by 28 publications

References 62 publications

Chemically specific coarse‐graining of polymers: Methods and prospects

Chemically specific coarse‐graining of polymers: Methods and prospects

DPD Modelling of the Self- and Co-Assembly of Polymers and Polyelectrolytes in Aqueous Media: Impact on Polymer Science

Predicting aggregate morphology of sequence-defined macromolecules with Recurrent Neural Networks

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