Bottom-up coarse-graining of polymers is commonly performed by matching structural order parameters such as distribution of bond lengths, bending and dihedral angles, and pair distribution functions. In this study, we introduce the distribution of nearest-neighbors as an additional order parameter in the concept of local density potentials. We describe how the inverse-Monte Carlo method provides a framework for forcefield development that is capable of overcoming challenges associated with the parameterization of interaction terms in polymer systems. The technique is applied on polyisoprene melts as a prototype system. We demonstrate that while different forcefields can be developed that perform equally in terms of matching target distributions, the inclusion of nearest-neighbors provides a straightforward route to match both thermodynamic and conformational properties. We find that several temperature state points can also be addressed, provided that the forcefield is refined accordingly. Finally, we examine both the single-particle and the collective dynamics of the coarse-grain models, demonstrating that all forcefields present a similar acceleration relative to the atomistic systems.
Coarse-graining of polyisoprene melts using inverse Monte Carlo and local density potentials
Nobahar Shahidi, Antonis Chazirakis, Vagelis Harmandaris, Manolis Doxastakis; Coarse-graining of polyisoprene melts using inverse Monte Carlo and local density potentials. J. Chem. Phys. 31 March 2020; 152 (12): 124902. https://doi.org/10.1063/1.5143245
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