A weak convergence viewpoint on invertible coarse-graining

Lennard-Jones Centre discussion group seminar by Prof. Grant M. Rotskoff from Stanford University.

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. This talk describes an approach that combines force-matching based coarse-graining with invertible neural networks to invert a coarse-graining map in a statistically precise fashion. It is shown that for non-trivial biomolecular systems, it is possible to recover the fine-grained free energy surface from coarse-grained sampling.

The seminar was held on 28th November 2022.

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