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Open AccessProceedings

Systematic Coarse-Grained Models for Molecular Systems Using Entropy

1
Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, Heraklion, GR 70013, Greece;
2
Department of Mathematics and Applied Mathematics, University of Crete & Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, Heraklion, GR 70013,Greece;
3
Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA 01003, USA
*
Authors to whom correspondence should be addressed.
Presented at the 5th International Electronic Conference on Entropy and Its Applications, 18–30 November 2019; Available online: https://ecea-5.sciforum.net/.
Proceedings 2020, 46(1), 27; https://doi.org/10.3390/ecea-5-06710
Published: 8 April 2020
The development of systematic coarse-grained mesoscopic models for complex molecular systems is an intense research area. Here we first give an overview of different methods for obtaining optimal parametrized coarse-grained models, starting from detailed atomistic representation for high dimensional molecular systems. We focus on methods based on information theory, such as relative entropy, showing that they provide parameterizations of coarse-grained models at equilibrium by minimizing a fitting functional over a parameter space. We also connect them with structural-based (inverse Boltzmann) and force matching methods. All the methods mentioned in principle are employed to approximate a many-body potential, the (n-body) potential of mean force, describing the equilibrium distribution of coarse-grained sites observed in simulations of atomically detailed models. We also present in a mathematically consistent way the entropy and force matching methods and their equivalence, which we derive for general nonlinear coarse-graining maps. We apply, and compare, the above-described methodologies in several molecular systems: A simple fluid (methane), water and a polymer (polyethylene) bulk system. Finally, for the latter we also provide reliable confidence intervals using a statistical analysis resampling technique, the bootstrap method.
Keywords: coarse-graining; data-driven; relative entropy; path-space; uncertainty quantification coarse-graining; data-driven; relative entropy; path-space; uncertainty quantification
MDPI and ACS Style

Kalligiannaki, E.; Harmandaris, V.; Katsoulakis, M. Systematic Coarse-Grained Models for Molecular Systems Using Entropy. Proceedings 2020, 46, 27.

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