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Predicting Structures of Ru-Centered Dyes: A Computational Screening Tool



Lisa A. Fredin, Thomas C. Allison


Dye-sensitized solar cells (DSCs) represent a viable means for harvesting solar energy to produce electrical power. Though a number of light harvesting dyes are in use, the search continues for more efficient and effective compounds to make commercially viable DSCs a reality. Computational methods have been increasingly applied to understand the dyes currently in use and to aid in the search for improved light harvesting compounds. Semiempirical quantum chemistry methods have a well-deserved reputation for giving good quality results in a very short amount of computer time. The most recent semiempirical models such as PM6 and PM7 are parameterized for a wide variety of molecule types, including organometallic complexes similar to DSC chromophores. In this article, the performance of PM6 is tested against a set of 20 molecules whose geometries were optimized using a density functional theory (DFT) method. It is found that PM6 gives geometries that are in good agreement with the optimized DFT structures. In order to reduce the differences between geometries optimized using PM6 and geometries optimized using DFT, the PM6 basis set parameters have been optimized for a subset of the molecules. It is found that it is sufficient to optimize the basis set for Ru alone to improve the agreement between the PM6 results and the DFT results. When this optimized Ru basis set is used, the mean unsigned error in Ru-ligand bond lengths is reduced from 0.043 ̊A to 0.017 ̊A in the set of 20 test molecules. These results clearly demonstrate the value of using PM6 to screen DSC chromophores as well as the value of optimizing PM6 basis set parameters for a specific set of molecules.
Journal of Chemical Physics


dye-sensitized solar cells, semiempirical quantum chemistry


Fredin, L. and Allison, T. (2016), Predicting Structures of Ru-Centered Dyes: A Computational Screening Tool, Journal of Chemical Physics, [online], (Accessed June 14, 2024)


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Created March 16, 2016, Updated February 19, 2017