(272c) Implementation and Automation of a Hierarchical Graph Based Approach for Extracting Coarse-Grain Mapping Operators

Coarse-grain (CG) molecular dynamics (MD) simulation allows us to overcome the limitations of all-atom (AA) MD simulation in terms of length and time scales. While CG-MD has been successfully applied to various systems like protein folding, there is still a need for a general guideline to determine the choice of CG mapping operators. Recently we have proposed a technique of incorporating multiple CG representations of a molecule within a single hierarchical graph. With the ability to extract valid CG mapping operators from the graph and relevant thermodynamic property like entropy driving the selection of one among the various mapping operators extractable from the hierarchical graph, this technique is a step towards automating the selection of CG mapping operators. As a proof of concept we have built a hierarchical graph for methanol which encodes all symmetry preserving CG mapping operators. We also demonstrated the feasibility of using the graph for automated CG mapping operator selection by using the uniform entropy flattening method to select CG mapping operators for methanol. In this talk, we will describe the implementation of the algorithm along with an automated pipeline framework to create a database of benchmark CG simulations. The discussion will include different thermodynamic measures to compare the CG and the AA performances. The large data set of CG simulations will aid in the development of standardized and scalable CG mapping strategy that is system independent.