Chemically specific coarse‐graining of polymers: Methods and prospects

Abstract

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.

Featurization strategies for polymer sequence or composition design by machine learning

“…[5][6][7][8][9] For example, using a limited set of just three different monomer types, there are on the order of 10 47 distinct copolymers that can be generated with degree of polymerization between 10 and 100. Thus, while theory and modeling are invaluable for understanding the origins of observed phenomena and informing the design of specific, well-defined polymer systems, [10][11][12][13][14][15][16][17] intricate studies may severely limit exposure to unknown but promising regions of design space. 18 In addition, resource limitations (time, monetary, or computational) likely preclude exhaustive characterization of combinatorial search spaces.…”

Computer Simulations of Deep Eutectic Solvents: Challenges, Solutions, and Perspectives

“…As in the case of all-atom MD, the choice of the FF is of critical importance. In general, the determination of interaction parameters is carried out individually for each calculated system, taking into account the specifics of the mapping scheme to convert an all-atom representation into a coarse-grained one [ 162 , 169 , 170 , 171 , 172 ].…”

Investigation of Machine Learning-based Coarse-Grained Mapping Schemes for Organic Molecules

“…Coarse-Grained (CG) molecular simulations [1] allow to access larger length and timescales, thereby expanding the reach of molecular modelling, according to the coarse graining level adopted. Macromolecules, such as polymers, are indeed systems that stand to greatly benefit from the utilization of a CG molecular description [2], to overcome the time and length scale limitations of a full atomistic representation, at the cost of losing part of the fine-grain chemical detail [3,4].…”