Ensuring thermodynamic consistency with invertible coarse-graining


Coarse-grained models are a core computational tool in theoretical chemistry and biophysics. A judicious choice of a coarse-grained model can yield physical insight by isolating the essential degrees of freedom that dictate the thermodynamic properties of a complex, condensed-phase system. The reduced complexity of the model typically leads to lower computational costs and more efficient sampling compared to atomistic models. Designing ``good’’ coarse-grained models is an art. Generally, the mapping from fine-grained configurations to coarse-grained configurations itself is not optimized in any way; instead, the energy function associated with the mapped configurations is. In this work, we explore the consequences of optimizing the coarse-grained representation alongside its potential energy function. We use a graph machine learning framework to embed atomic configurations into a low dimensional space to produce efficient representations of the original molecular system. Because the representation we obtain is no longer directly interpretable as a real space representation of the atomic coordinates, we also introduce an inversion process and an associated thermodynamic consistency relation that allows us to rigorously sample fine-grained configurations conditioned on the coarse-grained sampling. We show that this technique is robust, recovering the first two moments of the distribution of several observables in proteins such as chignolin and alanine dipeptide.

J. Chem. Phys. (Editor’s Pick)
Graduate Student

Shriram is a PhD student in the Department of Chemistry at Stanford University, where he is advised by Grant Rotskoff. He is broadly interested in utilizing both data driven and theoretical approaches to more robustly simulate and characterize biological systems. His current work involves using Reinforcement Learning methods to control systems driven away from equilibrium. Shriram received a B.S. in Biological Chemistry with Honors from The University of Chicago, where he also completed a minor in Computer Science. At UChicago, Shriram worked on quantifying the kinetics of small RNA molecule regulation under the mentorship of Jingyi Fei. Outside the lab, Shriram is an avid Chicago sports fan, enjoys watching movies and exploring the local restaurant scene.