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构建分子π轨道活动空间用于共轭体系的多参考计算。

Constructing Molecular π-Orbital Active Spaces for Multireference Calculations of Conjugated Systems.

机构信息

Department of Chemistry , Yale University , 225 Prospect Street , New Haven , Connecticut 06520 , United States.

出版信息

J Chem Theory Comput. 2019 Mar 12;15(3):1679-1689. doi: 10.1021/acs.jctc.8b01196. Epub 2019 Feb 18.

DOI:10.1021/acs.jctc.8b01196
PMID:30689378
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6526033/
Abstract

Molecules with conjugated π systems often feature strong electron correlation and therefore require multireference methods for a reliable computational description. A key prerequisite for the successful application of such methods is the choice of a suitable active space. Herein the automated π-orbital space (PiOS) method for selecting active spaces for multireference calculations of conjugated π systems is presented. This approach allows the construction of small but effective active spaces based on Hückel theory. To demonstrate its performance, π → π* excitations for benzene, octatetraene, and free-base porphine are computed. In addition, this technique can be combined with the automated atomic valence active space method to compute excitations in complex systems with multiple conjugated fragments. This combined approach was used to generate two-dimensional potential energy surfaces for multiple electronic states associated with photoinduced electron-coupled double proton transfer in the blue-light-using flavin photoreceptor protein. These types of methods for the automated selection of active space orbitals are important for ensuring consistency and reproducibility of multireference approaches for a wide range of chemical and biological systems.

摘要

具有共轭π体系的分子通常具有较强的电子相关,因此需要使用多参考方法进行可靠的计算描述。成功应用此类方法的关键前提是选择合适的活性空间。本文介绍了一种用于选择多参考计算共轭π体系的活性空间的自动π轨道空间(PiOS)方法。该方法允许根据休克尔理论构建小而有效的活性空间。为了演示其性能,计算了苯、辛四烯和自由碱基卟啉的π→π*激发。此外,该技术可以与自动原子价活性空间方法结合,用于计算具有多个共轭片段的复杂系统中的激发。这种组合方法用于生成与蓝光使用黄素光受体蛋白中的光诱导电子耦合双质子转移相关的多个电子态的二维势能面。对于广泛的化学和生物系统,用于自动选择活性空间轨道的此类方法对于确保多参考方法的一致性和可重复性非常重要。

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