Optimizing probability of barrier crossing with differentiable simulators

Martin Sipka, Johannes C. B. Dietschreit, Michal Pavelka, Lukáš Grajciar, Rafael Gomez-Bombarelli

Keywords: Differentiable simulations, enhanced sampling, neural differential equations

TL;DR: Method on how to define probability of barrier crossing and make it differentiable.

Abstract: Simulating events that involve some energy barrier often requires us to promote the barrier crossing in order to increase the probability of the event. One example of such a system can be a chemical reaction which we propose to explore using differentiable simulations. Transition path discovery and estimation of the reaction barrier are merged into a single end-to-end problem that is solved by path-integral optimization. We show how the probability of transition can be formulated in a differentiable way and increase it by introducing a trainable position dependent bias function. We also introduce improvements over standard methods making DiffSim training stable and efficient.

Submission Number: 67