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 be

SUBMITTER: Hase F 

PROVIDER: S-EPMC5863613 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature