Quantum transport in the FMO photosynthetic light-harvesting complex

One-Shot Trajectory Learning of Open Quantum Systems Dynamics

Nonadiabatic quantum dynamics is important for understanding light-harvesting processes, but its propagation with traditional methods can be rather expensive. Here we present a one-shot trajectory learning approach that allows us to directly make an ultrafast prediction of the entire trajectory of the reduced density matrix for a new set of such simulation parameters as temperature and reorganization energy. The whole 10-ps-long propagation takes 70 ms as we demonstrate on the comparatively large quantum system, the Fenna-Matthews-Olsen (FMO) complex. Our approach also significantly reduces time and memory requirements for training.

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

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 propagation in contrast to the traditional QD and alternative, recursive AI-based QD approaches. Our trajectory-learning AI-QD approach is able to predict the correct asymptotic behavior of QD at infinite time. We demonstrate AI-QD on seven-sites Fenna–Matthews–Olson (FMO) complex.

Simulations of energy transfer in light-harvesting complexes are computationally very demanding. Here the authors apply an artificial intelligence quantum dissipative algorithm to study the excited state energy transfer dynamics in a light-harvesting complex.

Superradiance and Directional Exciton Migration in Metal-Organic Frameworks

Crystalline metal-organic frameworks (MOFs) are promising synthetic analogues of photosynthetic light-harvesting complexes (LHCs). The precise assembly of linkers (organic chromophores) around the topology-defined pores offers the evolution of unique photophysical behaviors that are reminiscence of LHCs. These include MOF excited states with photoabsorbed energy that is spatially dispersed over multiple linkers defining the molecular excitons. The multilinker molecular excitons display superradiance─a hallmark of coupled oscillators seen in LHCs─with radiative rate constant (k_rad) exceeding that of a single linker. Our theoretical model and experimental results on three zirconium MOFs, namely, PCN-222(Zn), NU-1000, and SIU-100, with similar topology but varying linkers suggest that the size of such molecular excitons depends on the electronic symmetry of the linker. This multilinker exciton model effectively predicts the energy transfer rate constant; corresponding single-step exciton hopping time, ranging from a few picoseconds in SIU-100 and NU-1000 to a few hundreds of picoseconds in PCN-222(Zn), matches well with the experimental data. The model also predicts the anisotropy of exciton displacement with preferential migration along the crystallographic c-axis. Overall, these findings establish various missing links defining the exciton size and dynamics in MOF-assembled linkers. The understandings will provide design principles, especially, positioning the catalysts or electrode relative to the linker orientation for low-density solar energy conversion systems.

Analysis of Photosynthetic Systems and Their Applications with Mathematical and Computational Models

In biological and life science applications, photosynthesis is an important process that involves the absorption and transformation of sunlight into chemical energy. During the photosynthesis process, the light photons are captured by the green chlorophyll pigments in their photosynthetic antennae and further funneled to the reaction center. One of the most important light harvesting complexes that are highly important in the study of photosynthesis is the membrane-attached Fenna–Matthews–Olson (FMO) complex found in the green sulfur bacteria. In this review, we discuss the mathematical formulations and computational modeling of some of the light harvesting complexes including FMO. The most recent research developments in the photosynthetic light harvesting complexes are thoroughly discussed. The theoretical background related to the spectral density, quantum coherence and density functional theory has been elaborated. Furthermore, details about the transfer and excitation of energy in different sites of the FMO complex along with other vital photosynthetic light harvesting complexes have also been provided. Finally, we conclude this review by providing the current and potential applications in environmental science, energy, health and medicine, where such mathematical and computational studies of the photosynthesis and the light harvesting complexes can be readily integrated.