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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

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Ensuring thermodynamic consistency with invertible coarse-graining.

Shriram Chennakesavalu¹, David J Toomer¹, Grant M Rotskoff¹

¹Department of Chemistry, Stanford University, Stanford, California 94305, USA.

The Journal of Chemical Physics

|April 1, 2023

Summary

This study optimizes coarse-grained models by enhancing their representations and energy functions using graph machine learning. This approach improves the accuracy of molecular simulations for complex systems like proteins.

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Area of Science:

Computational Chemistry
Biophysics
Machine Learning

Background:

Coarse-grained models simplify complex molecular systems for computational efficiency.
Current models often lack optimization in their configuration mapping, focusing solely on energy functions.
Designing effective coarse-grained models remains a significant challenge in theoretical chemistry.

Purpose of the Study:

To investigate the impact of optimizing both the coarse-grained representation and its potential energy function.
To develop a novel framework for enhancing the design of coarse-grained models.
To improve the accuracy and efficiency of molecular simulations.

Main Methods:

Utilized a graph machine learning framework to embed atomic configurations into a low-dimensional space.
Developed an inversion process to reconstruct fine-grained configurations from the learned representation.
Introduced a thermodynamic consistency relation to ensure rigorous sampling.

Main Results:

Demonstrated a robust technique for optimizing coarse-grained models.
Successfully recovered the first two moments of observable distributions in proteins like chignolin and alanine dipeptide.
Showcased the effectiveness of jointly optimizing representation and energy function.

Conclusions:

Optimizing the coarse-grained representation alongside the potential energy function significantly enhances model performance.
The proposed graph machine learning framework and inversion process offer a rigorous approach to coarse-grained modeling.
This method provides a powerful tool for advancing molecular simulations in chemistry and biophysics.