Optimizing Probability of Barrier Crossing with Differentiable Simulators
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.
Cite
Text
Sipka et al. "Optimizing Probability of Barrier Crossing with Differentiable Simulators." ICML 2023 Workshops: Differentiable_Almost_Everything, 2023.Markdown
[Sipka et al. "Optimizing Probability of Barrier Crossing with Differentiable Simulators." ICML 2023 Workshops: Differentiable_Almost_Everything, 2023.](https://mlanthology.org/icmlw/2023/sipka2023icmlw-optimizing/)BibTeX
@inproceedings{sipka2023icmlw-optimizing,
title = {{Optimizing Probability of Barrier Crossing with Differentiable Simulators}},
author = {Sipka, Martin and Dietschreit, Johannes C. B. and Pavelka, Michal and Grajciar, Lukáš and Gomez-Bombarelli, Rafael},
booktitle = {ICML 2023 Workshops: Differentiable_Almost_Everything},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/sipka2023icmlw-optimizing/}
}