关于HRPA(D)在NMR自旋-自旋耦合常数计算中的性能：较小分子、芳香族和氟代芳香族化合物
On the performance of HRPA(D) for NMR spin-spin coupling constants: Smaller molecules, aromatic and fluoroaromatic compounds
AI总结：
本研究评估HRPA(D)在NMR自旋-自旋耦合常数计算中的性能，发现其与SOPPA相当或更优且成本更低，并确定CCSD(T)和MP2分别作为SOPPA和HRPA(D)的最佳几何优化方法。
摘要：
本研究考察了双修正高阶随机相位近似（HRPA(D)）在计算58个分子的NMR自旋-自旋耦合常数（SSCCs）时的性能，并以实验值作为参考值。HRPA(D)是二阶极化传播子近似（SOPPA）的一种近似，因此其计算成本低于SOPPA。HRPA(D)的性能与SOPPA相当，有时甚至更优，因此在计算SSCCs时应被视为SOPPA的替代方案。此外，还研究了对于8个较小分子，CCSD(T)或MP2几何优化是否对SOPPA和HRPA(D)的SSCCs计算最优。CCSD(T)是SOPPA计算的最佳几何优化方法，而MP2是HRPA(D) SSCC计算的最佳选择。
英文摘要：
In this study, the performance of the doubles-corrected higher random-phase approximation (HRPA(D)) has been investigated in calculations of NMR spin-spin coupling constants (SSCCs) for 58 molecules with the experimental values used as the reference values. HRPA(D) is an approximation to the second-order polarization propagator approximation (SOPPA), and is therefore computationally less expensive than SOPPA. HRPA(D) performs comparable and sometimes even better than SOPPA, and therefore when calculating SSCCs it should be considered as an alternative to SOPPA. Furthermore, it was investigated whether a CCSD(T) or MP2 geometry optimization was optimal for a SOPPA and a HRPA(D) SSCCs calculation for 8 smaller molecules. CCSD(T) is the optimal geometry optimization for the SOPPA calculation, and MP2 was optimal for the HRPA(D) SSCC calculations.
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