Predicting rate kernels via dynamic mode decomposition

Simulating dynamics of open quantum systems is sometimes a significant challenge, despite the availability of various exact or approximate methods. Particularly when dealing with complex systems, the huge computational cost will largely limit the applicability of these methods. In this work, we inve...

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Bibliographic Details
Published in:The Journal of chemical physics 2023-10, Vol.159 (14)
Main Authors: Liu, Wei, Chen, Zi-Hao, Su, Yu, Wang, Yao, Dou, Wenjie
Format: Article
Language:English
Subjects:
Complex systems
Computational efficiency
Computing costs
Decomposition
Kernels
Model reduction
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Summary:Simulating dynamics of open quantum systems is sometimes a significant challenge, despite the availability of various exact or approximate methods. Particularly when dealing with complex systems, the huge computational cost will largely limit the applicability of these methods. In this work, we investigate the usage of dynamic mode decomposition (DMD) to evaluate the rate kernels in quantum rate processes. DMD is a data-driven model reduction technique that characterizes the rate kernels using snapshots collected from a small time window, allowing us to predict the long-term behaviors with only a limited number of samples. Our investigations show that whether the external field is involved or not, the DMD can give accurate prediction of the result compared with the traditional propagations, and simultaneously reduce the required computational cost.
ISSN:0021-9606
1089-7690
DOI:10.1063/5.0170512