All Outputs (3)

Reinforcement learning of rare diffusive dynamics (2021)
Journal Article
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

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... Read More about Reinforcement learning of rare diffusive dynamics.

Quantum jump Monte Carlo approach simplified: Abelian symmetries (2021)
Journal Article
Macieszczak, K., & Rose, D. C. (2021). Quantum jump Monte Carlo approach simplified: Abelian symmetries. Physical Review A, 103(4), Article 042204. https://doi.org/10.1103/physreva.103.042204

We consider Markovian dynamics of a finitely dimensional open quantum system featuring a weak unitary symmetry, i.e., when the action of a unitary symmetry on the space of density matrices commutes with the master operator governing the dynamics. We... Read More about Quantum jump Monte Carlo approach simplified: Abelian symmetries.

A reinforcement learning approach to rare trajectory sampling (2021)
Journal Article
Rose, D. C., Mair, J. F., & Garrahan, J. P. (2021). A reinforcement learning approach to rare trajectory sampling. New Journal of Physics, 23, Article 013013. https://doi.org/10.1088/1367-2630/abd7bd

Very often when studying non-equilibrium systems one is interested in analysing dynamical behaviour that occurs with very low probability, so called rare events. In practice, since rare events are by definition atypical, they are often difficult to a... Read More about A reinforcement learning approach to rare trajectory sampling.