Encoding and Selecting Coarse-Grain Mapping Operators with Hierarchical Graphs

TL;DR

This paper introduces a hierarchical graph-based method to systematically encode and select coarse-grain mapping operators in molecular dynamics, reducing the combinatorial complexity by leveraging molecular topology and symmetry constraints.

Contribution

It presents a novel hierarchical graph encoding approach for coarse-grain mappings, enabling automated selection and reducing the combinatorial explosion of possible mappings.

Findings

Hierarchical graphs effectively encode multiple CG mapping operators.

Topology and symmetry constraints significantly reduce mapping options.

Demonstrated on methanol and a peptide, facilitating automated mapping selection.

Abstract

Coarse grain (CG) molecular dynamics (MD) can simulate systems inaccessible to fine grain (FG) MD simulations. A CG simulation decreases the degrees of freedom by mapping atoms from an FG representation into agglomerate CG particles. The FG to CG mapping is not unique. Research into systematic selection of these mappings is challenging due to their combinatorial growth with respect to the number of atoms in a molecule. Here we present a method of reducing the total count of mappings by imposing molecular topology and symmetry constraints. The count reduction is illustrated by considering all mappings for nearly 49,889 molecules. The resulting number of mapping operators is still large, so we introduce hierarchical graphs which encode multiple CG mapping operators. The encoding method is demonstrated for methanol and a 14-mer peptide. This encoding provides a foundation to perform automated mapping selection.