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

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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

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Publication details

The article was received on 13 Aug 2017, accepted on 23 Oct 2017 and first published on 23 Oct 2017


Article type: Edge Article
DOI: 10.1039/C7SC03542J
Citation: Chem. Sci., 2017, Advance Article
  • Open access: Creative Commons BY license
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    Machine learning for quantum dynamics: deep learning of excitation energy transfer properties

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

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