Optimizing probability of barrier crossing with differentiable simulators

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.