定域耦合簇方法在中等强相关区域的性能：膨胀卟啉的Hückel-Möbius相互转换
Performance of Localized Coupled Cluster Methods in a Moderately Strong Correlation Regime: Hückel-Möbius Interconversions in Expanded Porphyrins
摘要：
定域轨道耦合集团理论最近出现作为一个非经验的替代DFT的大系统。直觉上，人们可能会期望这样的方法在高度离域的系统中表现得不太好。在目前的工作中，我们应用规范的CCSD（T）和各种本地化的近似，以一组扩展的卟啉-系统，可以切换之间的休克尔，图8，和莫比乌斯拓扑结构在外部刺激。同时考虑了最小值和异构化过渡态。我们发现Möbius（-like）结构比其他结构具有更强的静态相关特性，这会导致DLPNO-CCSD（T）甚至DLPNO-CCSD（T1）方法的显著误差，除非采用TightPNO截断。如果即使对于莫比乌斯型系统（或其他受强静态相关困扰的系统）也需要亚kcal/mol的正则相对能量再现，那么Nagy和Kallay的LNO-CCSD（T）方法可以提供“紧密”设置，其计算费用比PNO-LCCSD（T）或DLPNO-LCCSD（T）方法大得多，但仍然比正则方法温和得多。我们将提出目前的POLYPYR 21数据集作为局部轨道方法的基准，或者更广泛地说，作为较低级别方法处理具有强烈不同程度静态相关性的能量学的能力的基准。
英文摘要：
Localized orbital coupled cluster theory has recently emerged as an nonempirical alternative to DFT for large systems. Intuitively, one might expect such methods to perform less well for highly delocalized systems. In the present work, we apply both canonical CCSD(T) and a variety of localized approximations thereto to a set of expanded porphyrins -- systems that can switch between Hückel, figure-eight, and Möbius topologies under external stimuli. Both minima and isomerization transition states are considered. We find that Möbius(-like) structures have much stronger static correlation character than the remaining structures, and that this causes significant errors in DLPNO-CCSD(T) and even DLPNO-CCSD(T1) approaches, unless TightPNO cutoffs are employed. If sub-kcal/mol reproduction of canonical relative energies is required even for Möbius-type systems (or other systems plagued by strong static correlation), then Nagy and Kallay's LNO-CCSD(T) method with "tight" settings can provide that, at much greater computational expense than either the PNO-LCCSD(T) or DLPNO-LCCSD(T) approaches but with still a much gentler CPU time scaling than canonical approaches. We would propose the present POLYPYR21 dataset as a benchmark for localized orbital methods, or more broadly, for the ability of lower-level methods to handle energetics with strongly varying degrees of static correlation.
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