Abstract

In probability theory, the notion of “weak convergence” is often used to describe two equivalent probability distributions. This relaxed metric requires equivalence of the average value of any function under the two probability distributions being compared. In coarse-graining, Noid and Voth developed a thermodynamic equivalence principle that has a similar requirement. Nevertheless, there are many functions of the fine-grained system that we simply cannot evaluate on the coarse-grained degrees of freedom. In this talk, I will describe an approach that combines force-matching based coarse-graining with invertible neural networks to invert a coarse-graining map in a statistically precise fashion. I will show that for non-trivial biomolecular systems, we can recover the fine-grained free energy surface from coarse-grained sampling.