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Cited by 51Spectroscopy and Quantum Chemical StudiesPhotochemistry and Electron Transfer StudiesStrong Light-Matter Interactions

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Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics

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Ullah, Arif, Dral, Pavlo O.·2022·Nature Communications
DOI 10.1038/s41467-022-29621-wPMC PMC9001686

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Abstract

Exploring excitation energy transfer (EET) in light-harvesting complexes (LHCs) is essential for understanding the natural processes and design of highly-efficient photovoltaic devices. LHCs are open systems, where quantum effects may play a crucial role for almost perfect utilization of solar energy. Simulation of energy transfer with inclusion of quantum effects can be done within the framework of dissipative quantum dynamics (QD), which are computationally expensive. Thus, artificial intelligence (AI) offers itself as a tool for reducing the computational cost. Here we suggest AI-QD approach using AI to directly predict QD as a function of time and other parameters such as temperature, reorganization energy, etc., completely circumventing the need of recursive step-wise dynamics propagation in contrast to the traditional QD and alternative, recursive AI-based QD approaches. Our trajectory-learning AI-QD approach is able to predict the correct asymptotic behavior of QD at infinite time. We demonstrate AI-QD on seven-sites Fenna–Matthews–Olson (FMO) complex.

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Metadata

Authors
Ullah, A., & Dral, P. O.
Affiliations
Xiamen University (CN)
Corresponding author
  • Ullah, Arif
  • Dral, Pavlo O.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2022-04-11
Volume
13
Issue
1
Pages
1930-1930
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-022-29621-w
PMC
PMC9001686
OpenAlex
W4223485577
Cited by
51
References
6
Citations by year
2026: 3 · 2025: 13 · 2024: 14 · 2023: 11 · 2022: 10
FWCI
4.86
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • ChemRxiv (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (22003051)
  • Fundamental Research Funds for the Central Universities (20720210092)
Topics
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Spectroscopy and Quantum Chemical Studies
  • Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Photochemistry and Electron Transfer Studies
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Strong Light-Matter Interactions
Keywords
excitation energy transfer, light-harvesting complexes, dissipative quantum dynamics, artificial intelligence, trajectory learning, Fenna-Matthews-Olson complex, reorganization energy, temperature effects, photovoltaic devices, solar energy utilization
MeSH terms
Artificial Intelligence, Bacterial Proteins, Energy Transfer, Quantum Theory, Light-Harvesting Protein Complexes

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