CAREER: Novel QM/MM algorithms for understanding efficient energy harvesting in photosynthesis

普渡大学的柳德米拉·斯利普琴科得到了理论、模型和计算方法计划颁发的奖项的支持，该计划旨在开发计算方法，使自然和人工光合作用复合体中的能量转移能够准确和预测地建模，包括蛋白质环境的显式溶剂效应。这些方法基于量子力学/分子力学(QM/MM)方法，其中QM部分使用精确的激发态方法，MM部分使用基于从头算的有效碎片势(EFP)方法。EFP方法克服了现有QM/MM技术的最大局限性，用基于无参数第一原理的项取代了经验相互作用，而不牺牲计算效率。Pi和她的研究小组正在开发计算工具，能够对自然和人工光合作用复合体中的能量传递进行准确和预测的建模，包括蛋白质环境的显式影响。这项研究增加了我们对能量收集的理解，并有助于高效光伏设备和光合作用系统的工程设计。这项研究在计算机代码设计中有很强的组成部分。由此产生的软件被集成到广泛使用的量子化学代码中，并可供广大科学家和工程师使用。这项研究得到了与数字基础设施办公室共同资助的安排的支持。

Lyudmila Slipchenko of Purdue University is supported by an award from the Theory, Models and Computational Method program to develop computational methods that enable accurate and predictive modeling of energy transfer in natural and artificial photosynthetic complexes, including explicit solvent effects of the protein environment. The methods are based on the quantum mechanics/molecular mechanics (QM/MM) approach in which accurate excited state methods are used for the QM part and the ab initio-based effective fragment potential (EFP) method is used for the MM part. The EFP method overcomes the most significant limitation of existing QM/MM techniques by replacing empirical interactions by parameter-free first principles based terms, without a sacrifice in computational efficiency.The PI and her research group are developing computational tools that enable accurate and predictive modeling of energy transfer in natural and artificial photosynthetic complexes, including the explicit effect of the protein environment. This research increases our understanding of energy harvesting and contributes to the engineering of efficient photovoltaic devices and photosynthetic systems. This research has a strong component of computer code design. The resulting software is integrated into widely used quantum chemistry codes and made available to the broad community of scientists and engineers. This research is supported in a co-funding arrangement with the Office of Cyberinfrastruture.