Graph-Based Approach to Systematic Molecular Coarse-Graining

Webb, Michael A.; Delannoy, Jean-Yves; de Pablo, Juan J.

doi:10.1021/acs.jctc.8b00920

Title: Graph-Based Approach to Systematic Molecular Coarse-Graining

Journal Article · 28 November 2018 · Journal of Chemical Theory and Computation

DOI: https://doi.org/10.1021/acs.jctc.8b00920 · OSTI ID:1558004

^[1]; Delannoy, Jean-Yves ^[2];

^[3]

Univ. of Chicago, IL (United States). Inst. for Molecular Engineering
Simulation, Modeling and Artificial Intelligence team, Solvay, Bristol, Pennsylvania 19007, United States
Univ. of Chicago, IL (United States). Inst. for Molecular Engineering; Argonne National Lab. (ANL), Argonne, IL (United States). Materials Science Division

A novel methodology is introduced here to generate coarse-grained (CG) representations of molecular models for simulations. The proposed strategy relies on basic graph-theoretic principles and is referred to as graph-based coarse-graining (GBCG). It treats a given system as a molecular graph and derives a corresponding CG representation by using edge contractions to combine nodes in the graph, which correspond to atoms in the molecule, into CG sites. A key element of this methodology is that the nodes are combined according to well-defined protocols that rank-order nodes based on the underlying chemical connectivity. By iteratively performing these operations, successively coarser representations of the original atomic system can be produced to yield a systematic set of CG mappings with hierarchical resolution in an automated fashion. These capabilities are demonstrated in the context of several test systems, including toluene, pentadecane, a polysaccharide dimer, and a rhodopsin protein. In these examples, GBCG yields multiple, intuitive structures that naturally preserve the chemical topology of the system. Importantly, these representations are rendered from algorithmic implementation rather than an arbitrary ansatz, which, until now, has been the conventional approach for defining CG mapping schemes. Overall, the results presented here indicate that GBCG is efficient, robust, and unambiguous in its application, making it a valuable tool for future CG modeling.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1558004

Journal Information:: Journal of Chemical Theory and Computation, Journal Name: Journal of Chemical Theory and Computation Journal Issue: 2 Vol. 15; ISSN 1549-9618

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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