Reinforcement learning of rare diffusive dynamics

Das, Avishek; Rose, Dominic C.; Garrahan, Juan P.; Limmer, David T.

Abstract

We present a method to probe rare molecular dynamics trajectories directly using reinforcement learning. We consider trajectories that are conditioned to transition between regions of configuration space in finite time, such as those relevant in the study of reactive events, and trajectories exhibiting rare fluctuations of time-integrated quantities in the long time limit, such as those relevant in the calculation of large deviation functions. In both cases, reinforcement learning techniques are used to optimize an added force that minimizes the Kullback-Leibler divergence between the conditioned trajectory ensemble and a driven one. Under the optimized added force, the system evolves the rare fluctuation as a typical one, affording a variational estimate of its likelihood in the original trajectory ensemble. Low variance gradients employing value functions are proposed to increase the convergence of the optimal force. The method we develop employing these gradients leads to efficient and accurate estimates of both the optimal force and the likelihood of the rare event for a variety of model systems.

Citation

Das, A., Rose, D. C., Garrahan, J. P., & Limmer, D. T. (2021). Reinforcement learning of rare diffusive dynamics. Journal of Chemical Physics, 155(13), Article 134105. https://doi.org/10.1063/5.0057323

• Journal Article Type: Article
• Acceptance Date: Sep 12, 2021
• Online Publication Date: Oct 1, 2021
• Publication Date: Oct 7, 2021
• Deposit Date: Nov 3, 2021
• Publicly Available Date: Nov 3, 2021
• Journal: Journal of Chemical Physics
• Print ISSN: 0021-9606
• Electronic ISSN: 1089-7690
• Publisher: AIP Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 155
• Issue: 13
• Article Number: 134105
• DOI: https://doi.org/10.1063/5.0057323
• Keywords: Physical and Theoretical Chemistry; General Physics and Astronomy
• Public URL: https://nottingham-repository.worktribe.com/output/6608231
• Publisher URL: https://aip.scitation.org/doi/full/10.1063/5.0057323
• Additional Information: This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Avishek Das, Dominic C. Rose, Juan P. Garrahan, and David T. Limmer , "Reinforcement learning of rare diffusive dynamics", J. Chem. Phys. 155, 134105 (2021) and may be found at https://doi.org/10.1063/5.0057323