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

Zhixuan Zhong^1,2, Lifeng Xu^1,2, Jian Jiang^1,2

¹Beijing National Laboratory for Molecular Sciences, State Key Laboratory of Polymer Physics and Chemistry, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, P. R. China.

Journal of Chemical Theory and Computation

|January 9, 2025

Summary

This summary is machine-generated.

We developed an automated framework for molecular simulation (AMOFMS) to streamline coarse-grained (CG) force field optimization. This tool uses a neural network for accurate atomistic-to-CG mapping, accelerating the development of high-precision molecular simulations.

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Area of Science:

Computational chemistry
Molecular modeling
Materials science

Background:

Accurate and efficient coarse-grained (CG) force fields are essential for simulating large, complex molecular systems.
Current methods for CG force field development often require significant manual intervention and can be time-consuming.

Purpose of the Study:

To present an automated mapping and optimization framework for molecular simulation (AMOFMS).
To streamline and enhance the process of developing high-precision CG force fields.
To reduce manual effort and accelerate the optimization of CG force fields.

Main Methods:

Developed a neural-network-based mapping function, DSGPM-TP (deep supervised graph partitioning model with type prediction), for atomistic-to-CG conversion.
Integrated bottom-up and top-down methodologies for flexible optimization targets.
Implemented a parallel optimizer to significantly accelerate the force field optimization process.

Main Results:

DSGPM-TP accurately and efficiently converts atomistic structures to CG mappings.
AMOFMS successfully optimized parameters for systems like POPC and PEO.
The framework demonstrated robustness and effectiveness in developing high-precision CG force fields.

Conclusions:

AMOFMS provides a general and flexible framework for automated CG force field development.
The automated approach significantly reduces the time and manual effort required for force field optimization.
This framework enables high-precision molecular simulations of large and complex systems.