Journal
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
Volume 12, Issue 9, Pages 2476-2483Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpclett.1c00079
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Funding
- startup funds of the College of Arts and Sciences
- Department of Physics and Astronomy of the University of Delaware
- University of Delaware
- Universidad Nacional de Colombia
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The study demonstrates that a deep artificial neural network with convolutional layers can efficiently and accurately predict the long-time dynamics of open quantum systems, offering new possibilities for the research of open quantum systems.
Exact numerical simulations of dynamics of open quantum systems often require immense computational resources. We demonstrate that a deep artificial neural network composed of convolutional layers is a powerful tool for predicting long-time dynamics of open quantum systems provided the preceding short-time evolution of a system is known. The neural network model developed in this work simulates long-time dynamics efficiently and accurately across different dynamical regimes from weakly damped coherent motion to incoherent relaxation. The model was trained on a data set relevant to photosynthetic excitation energy transfer and can be deployed to study long-lasting quantum coherence phenomena observed in light-harvesting complexes. Furthermore, our model performs well for the initial conditions different than those used in the training. Our approach reduces the required computational resources for long-time simulations and holds the promise for becoming a valuable tool in the study of open quantum systems.
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