Models and Dynamics for Energy and Charge Transfer in Light Harvesting

来自波士顿大学的大卫科克得到了化学理论、模型和计算方法项目的支持，该项目旨在开发新的理论和计算工具，以了解植物和细菌等自然系统以及合成系统中如何通过光合作用收获光。他开发了建模，分析和预测能量传输和电荷分离过程的光谱特征的方法，这些过程是光合作用的重要组成部分。 自然界已经开发出了在分子水平上调整纳米尺度结构的显着方法，以在不同条件下优化功能。在新的生物启发的纳米材料和技术中模仿这种多功能性需要对这些微观过程的工作原理有基本的了解。 这种认识目前缺乏或发展不足。在这项研究中探索的新的理论和计算方法将提供解释和指导实验的手段，目的是发展对这些系统行为的基本理解。 这些知识是设计用于光能收集的多功能分子纳米结构的关键。 在这个项目中，Coker和他的团队将首先专注于扩展，第一原理，激发态量子化学方法和构象采样技术，以计算在最近的超快非线性光谱研究中备受关注的生物光捕获系统模型中的参数分布。这种模型通常用来解释这些平均实验的结果。这些最佳拟合的平均模型具有许多难以估计的参数，并且它们通常不是唯一的，通常导致模糊的解释。然而，Coker小组正在开发的理论方法能够详细分析平均值的波动和独特模型的集合的采样，例如，包括高性能的结构异常值，其特征将为最佳设计提供重要的理解，而不是平均行为。在第二个项目中，耗散量子动力学方法被用来计算光谱特性和研究弛豫过程，包括能量传输和电荷分离使用计算模型的合奏。一套技术将实施范围从标准微扰近似，包括Redfield理论，和福斯特理论的变种，更一般的非微扰方法，包括路径积分，半经典，混合量子经典方法，如部分线性化密度矩阵动力学方法在以前的工作中开发。一个有效的，自动化的计划之间切换不同的动态处理将实施使用标准，以决定哪种方法将提供一个最佳的动态描述，平衡精度和可靠性与计算成本为一个给定的采样模型。这个计划的发展是很重要的，使这些广泛波动的模式有效的采样，因为合奏可能会探索模型在非常不同的动力学制度。这种自适应的方法来计算合奏动力学将基准对精确的计算，可以在较小的简化模型，然后才实施研究大规模的现实系统。

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.