A Neural-Network-Based Mapping and Optimization Framework for High-Precision Coarse-Grained Simulation

TL;DR

This paper introduces AMOFMS, an automated framework utilizing neural networks for efficient mapping and optimization of coarse-grained force fields, enhancing high-precision molecular simulations of large, complex systems.

Contribution

It presents a novel neural-network-based mapping function and an integrated optimization framework that automates and accelerates the development of high-precision coarse-grained force fields.

Findings

Successfully optimized parameters for POPC and PEO systems.

Significantly reduced optimization time with parallel processing.

Demonstrated robustness and flexibility of the framework.

Abstract

The accuracy and efficiency of a coarse-grained (CG) force field are pivotal for high-precision molecular simulations of large systems with complex molecules. We present an automated mapping and optimization framework for molecular simulation (AMOFMS), which is designed to streamline and improve the force field optimization process. It features a neural-network-based mapping function, DSGPM-TP (Deep Supervised Graph Partitioning Model with Type Prediction). This model can accurately and efficiently convert atomistic structures to CG mappings, reducing the need for manual intervention. By integrating bottom-up and top-down methodologies, AMOFMS allows users to freely combine these approaches or use them independently as optimization targets. Moreover, users can select and combine different optimizers to meet their specific mission. With its parallel optimizer, AMOFMS significantly accelerates the optimization process, reducing the time required to achieve optimal results. Successful applications of AMOFMS include parameter optimizations for systems such as POPC and PEO, demonstrating its robustness and effectiveness. Overall, AMOFMS provides a general and flexible framework for the automated development of high-precision CG force fields.