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预测钌中心染料的结构:一种计算筛选工具。

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

作者信息

Fredin Lisa A, Allison Thomas C

机构信息

Chemical Informatics Research Group, Chemical Science Division, Material Measurement Laboratory, National Institute of Standards and Technology , 100 Bureau Drive, Stop 8320, Gaithersburg, Maryland 20899-8320, United States.

出版信息

J Phys Chem A. 2016 Apr 7;120(13):2135-43. doi: 10.1021/acs.jpca.6b00921. Epub 2016 Mar 24.

Abstract

Dye-sensitized solar cells (DSCs) represent a 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 parametrized 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 to 0.017 Å in the set of 20 test molecules. Though the magnitude of these differences is small, the effect on the calculated UV/vis spectra is significant. 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.

摘要

染料敏化太阳能电池(DSCs)是一种收集太阳能以产生电能的方式。尽管目前正在使用多种光收集染料,但人们仍在继续寻找更高效、更有效的化合物,以使商业上可行的染料敏化太阳能电池成为现实。计算方法已越来越多地应用于理解当前使用的染料,并有助于寻找改进的光收集化合物。半经验量子化学方法在极短的计算机时间内就能给出高质量的结果,因而享有盛誉。最新的半经验模型,如PM6和PM7,针对多种分子类型进行了参数化,包括与染料敏化太阳能电池发色团类似的有机金属配合物。在本文中,针对一组20个分子测试了PM6的性能,这些分子的几何结构使用密度泛函理论(DFT)方法进行了优化。结果发现PM6给出的几何结构与优化后的DFT结构高度吻合。为了减少使用PM6优化的几何结构与使用DFT优化的几何结构之间的差异,已针对部分分子对PM6基组参数进行了优化。结果发现,仅对Ru优化基组就足以提高PM6结果与DFT结果之间的一致性。当使用这种优化的Ru基组时,在20个测试分子中,Ru-配体键长的平均无符号误差从0.043 Å降至0.017 Å。尽管这些差异的幅度很小,但对计算出的紫外/可见光谱的影响却很显著。这些结果清楚地证明了使用PM6筛选染料敏化太阳能电池发色团的价值,以及针对特定分子集优化PM6基组参数的价值。

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引用本文的文献

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Semiempirical configuration interaction calculations for ru-centered dyes.
J Comput Chem. 2018 Jul 15;39(19):1259-1266. doi: 10.1002/jcc.25190. Epub 2018 Feb 15.

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