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Adversarial-residual-coarse-graining: Applying machine learning theory to systematic molecular coarse-graining.

Aleksander E P Durumeric¹, Gregory A Voth¹

¹Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, and Computation Institute, The University of Chicago, Chicago, Illinois 60637, USA.

The Journal of Chemical Physics

|October 3, 2019

Summary

We introduce a novel framework for molecular coarse-graining (CG) by linking CG methods with machine learning generative models. This approach enables rigorous parameterization, even with virtual sites, offering new possibilities for molecular simulations.

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Area of Science:

Computational chemistry
Machine learning
Molecular modeling

Background:

Coarse-graining (CG) simplifies complex molecular systems for simulations.
Generative models offer powerful tools for data representation and generation.

Purpose of the Study:

To develop a systematic framework for molecular coarse-graining (CG) by integrating CG approaches with implicit generative models.
To explore the capabilities of generative adversarial networks (GANs) within this new CG framework.
To enable rigorous parameterization of CG models, including those with virtual sites.

Main Methods:

Utilizing connections between molecular coarse-graining (CG) and implicit generative models.
Focusing on the formalism of generative adversarial networks (GANs).
Demonstrating parameterization strategies, including those with virtual sites.

Main Results:

The framework allows for various parameterization strategies, some similar to existing CG methods.
It rigorously parameterizes CG models with virtual sites, which lack direct reference atomistic connections.
A limitation is the absence of a closed-form expression for the CG Hamiltonian after integrating over virtual sites.

Conclusions:

The developed framework provides a rigorous method for molecular coarse-graining (CG) using machine learning generative models.
It successfully parameterizes CG models with virtual sites, expanding CG capabilities.
The approach is applicable in cases where traditional methods like relative entropy minimization are not suitable.