ABSTRACT

David Coker from Boston University is supported by an award from the Chemical Theory, Models and Computational Methods program to develop new theoretical and computational tools for understanding how light is harvested through photosynthesis both in natural systems like plants and bacteria, and synthetic ones. He develops methods for modeling, analyzing and predicting spectroscopic signatures of energy transport and charge separation processes that are essential components of photosynthesis. Nature has developed remarkable ways of adapting nano-scale structures at the molecular level to optimize function under varying conditions. Mimicking this versatility in new bio-inspired nano-materials and technologies requires a fundamental understanding of the workings of these microscopic processes. Such understanding is currently missing or poorly developed. The new theoretical and computational methods explored in this research will provide the means to interpret and guide experiments with the goal of developing fundamental understanding of the behavior of these systems. Such knowledge is key to the design of versatile functional molecular nano-structures for light energy harvesting.


In this project, Coker and his team will first focus on extending, first principles, excited state quantum chemical methods and conformational sampling techniques to compute the distributions of parameters in models of the biological light harvesting systems that have received much attention in recent ultrafast nonlinear spectroscopy studies. Such models are usually employed to interpret the results of these averaged experiments. These best-fit, average models have many parameters that can be difficult to estimate and they are not generally unique, often leading to ambiguous interpretation. The theoretical methods being developed by the Coker group, however, enable detailed analysis of fluctuations underlying the average and the sampling of an ensemble of unique models that include, for example, highly performing structural outliers whose characteristics will give important understanding for optimal design, rather than mean behavior. In the second project, dissipative quantum dynamical methods are employed to compute spectroscopic properties and study relaxation processes including energy transport and charge separation using the ensembles of computed models. A suite of techniques will be implemented ranging from standard perturbative approximations including Redfield theory, and variants of Forster theory, to more general non-perturbative approaches including path-integral, semi-classical, and mixed quantum-classical methods like the partial linearized density matrix dynamics approaches developed in previous work. An efficient, automated scheme for switching between different dynamical treatments will be implemented using criteria to decide which approach will provide an optimal description of the dynamics, balancing accuracy and reliability with computational cost for a given sampled model. The development of this scheme is important to make the sampling of these widely fluctuating models efficient, as the ensemble may explore models in very different dynamical regimes. This adaptive approach to computing the ensemble dynamics will be benchmarked against exact calculations that can be performed on smaller simplified models before it is implemented to study large-scale realistic systems.

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