Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics.

ABSTRACT: Exploring excitation energy transfer (EET) in light-harvesting complexes (LHCs) is essential for understanding the natural processes and design of highly-efficient photovoltaic devices. LHCs are open systems, where quantum effects may play a crucial role for almost perfect utilization of solar energy. Simulation of energy transfer with inclusion of quantum effects can be done within the framework of dissipative quantum dynamics (QD), which are computationally expensive. Thus, artificial intelligence (AI) offers itself as a tool for reducing the computational cost. Here we suggest AI-QD approach using AI to directly predict QD as a function of time and other parameters such as temperature, reorganization energy, etc., completely circumventing the need of recursive step-wise dynamics propaga

SUBMITTER: Ullah A 

PROVIDER: S-EPMC9001686 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature