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 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