Issue 12, 2017

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 propose 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 exhibit similar or even higher accuracies than frequently used approximate methods such as secular Redfield theory.

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

Supplementary files

Article information

Article type
Edge Article
Submitted
13 Aug 2017
Accepted
23 Oct 2017
First published
23 Oct 2017
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2017,8, 8419-8426

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

F. Häse, C. Kreisbeck and A. Aspuru-Guzik, Chem. Sci., 2017, 8, 8419 DOI: 10.1039/C7SC03542J

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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