Theoretical and Computational Chemistry

One-shot trajectory learning of open quantum systems dynamics

Authors

Abstract

Nonadiabatic quantum dynamics are important for understanding light-harvesting processes, but their propagation with traditional methods can be rather expensive. Here we present a one-shot trajectory learning approach that allows to directly make ultra-fast prediction of the entire trajectory of the reduced density matrix for a new set of such simulation parameters as temperature and reorganization energy. The whole 10ps long propagation takes 70 milliseconds as we demonstrate on the comparatively large quantum system, the Fenna–Matthews–Olsen (FMO) complex. Our approach also significantly reduces time and memory requirements for training.

Version notes

(1) We extend the time-limit from 2.5ps to 10ps. (2) We predict the full reduced density matrix (3) We also show interpolation and extrapolation in the parameter space

Content

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Supplementary material

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Supporting Information for One-shot Trajectory Learning of Open Quantum Systems Dynamics
Supporting Information for One-shot Trajectory Learning of Open Quantum Systems Dynamics

Supplementary weblinks

Dral's group
The goal of research is accelerating and improving computational chemistry with artificial intelligence / machine learning.