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Title: Machine learning for quantum dynamics: deep learning of excitation energy transfer properties

Abstract

Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics. Natural light harvesting in photosynthesis shows remarkable excitation energy transfer properties, which suggests that pigment–protein complexes could serve as blueprints for the design of nature inspired devices. Mechanistic insights into energy transport dynamics can be gained by leveraging numerically involved propagation schemes such as the hierarchical equations of motion (HEOM). Solving these equations, however, is computationally costly due to the adverse scaling with the number of pigments. Therefore virtual high-throughput screening, which has become a powerful tool in material discovery, is less readily applicable for the search of novel excitonic devices. We present the use of artificial neural networks to bypass the computational limitations of established techniques for exploring the structure-dynamics relation in excitonic systems. Once trained, our neural networks reduce computational costs by several orders of magnitudes. Our predicted transfer times and transfer efficiencies demonstrate similar or even higher accuracies than frequently used approximate methods such as secular Redfield theory.

Authors:
 [1];  [1]; ORCiD logo [1]
+ Show Author Affiliations
  1. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, USA
Publication Date:
Research Org.:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1409485
Alternate Identifier(s):
OSTI ID: 1506094
Grant/Contract Number:  
SC0001088
Resource Type:
Published Article
Journal Name:
Chemical Science
Additional Journal Information:
Journal Name: Chemical Science Journal Volume: 8 Journal Issue: 12; Journal ID: ISSN 2041-6520
Publisher:
Royal Society of Chemistry (RSC)
Country of Publication:
United Kingdom
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Häse, Florian, Kreisbeck, Christoph, and Aspuru-Guzik, Alán. Machine learning for quantum dynamics: deep learning of excitation energy transfer properties. United Kingdom: N. p., 2017. Web. doi:10.1039/C7SC03542J.
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Häse, Florian, Kreisbeck, Christoph, & Aspuru-Guzik, Alán. Machine learning for quantum dynamics: deep learning of excitation energy transfer properties. United Kingdom. https://doi.org/10.1039/C7SC03542J
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Häse, Florian, Kreisbeck, Christoph, and Aspuru-Guzik, Alán. Sun . "Machine learning for quantum dynamics: deep learning of excitation energy transfer properties". United Kingdom. https://doi.org/10.1039/C7SC03542J.
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@article{osti_1409485,
title = {Machine learning for quantum dynamics: deep learning of excitation energy transfer properties},
author = {Häse, Florian and Kreisbeck, Christoph and Aspuru-Guzik, Alán},
abstractNote = {Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics. Natural light harvesting in photosynthesis shows remarkable excitation energy transfer properties, which suggests that pigment–protein complexes could serve as blueprints for the design of nature inspired devices. Mechanistic insights into energy transport dynamics can be gained by leveraging numerically involved propagation schemes such as the hierarchical equations of motion (HEOM). Solving these equations, however, is computationally costly due to the adverse scaling with the number of pigments. Therefore virtual high-throughput screening, which has become a powerful tool in material discovery, is less readily applicable for the search of novel excitonic devices. We present the use of artificial neural networks to bypass the computational limitations of established techniques for exploring the structure-dynamics relation in excitonic systems. Once trained, our neural networks reduce computational costs by several orders of magnitudes. Our predicted transfer times and transfer efficiencies demonstrate similar or even higher accuracies than frequently used approximate methods such as secular Redfield theory.},
doi = {10.1039/C7SC03542J},
journal = {Chemical Science},
number = 12,
volume = 8,
place = {United Kingdom},
year = {2017},
month = {1}
}
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https://doi.org/10.1039/C7SC03542J
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Cited by: 12 works
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Figures / Tables:

Fig. 1 Fig. 1: Machine learning excitation energy transfer properties in open quantum systems. (A) Fenna–Matthews–Olson (FMO) pigment–protein complex with eight chlorophyll pigments in the conventional numbering scheme. Dominant energy transfer pathways from the donor pigment 8 (blue) to the acceptor pigment 3 (orange) are indicated. (B) Results for average transfer timemore » $\langle$t$\rangle$ calculations for energy transfer in the FMO complex from the donor to the acceptor obtained from solving the hierarchical equations of motion (HEOM), the approximate secular Redfield formalism and predicted by multi-layer perceptrons (MLPs) designed in this study. Computational costs are reported for each method. (C) Illustration of the MLP architecture. MLPs accept Frenkel exciton Hamiltonians as input feature and predict average transfer times and efficiencies. The best network architectures were obtained through Bayesian optimization.« less
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