An accurate description of catalytic selectivity poses an enormous challenge for theoretical simulations. Due to the absence of a well-defined benchmark set on the catalytic selectivity, the performance of even the widely used density functional approximations (DFAs) is yet to be validated. This work reports a test set based on the selective hydrogenation of α,β-unsaturated aldehydes catalyzed by ruthenium (Ru) hydride complexes. Special attention is paid to benchmark the regioselectivity of aldehydes to either unsaturated alcohols or saturated aldehydes. Accurate reference data were calculated by the massive parallel implementation of the coupled-cluster single, double, and perturbative triple excitations [CCSD(T)] approach, based completely on set limits. Furthermore, we performed the microkinetic simulation based on the CCSD(T) energy profiles, serving the most direct criteria for the performance of DFAs on catalytic selectivity of hydrogenation reactions. Using this test set, we uncovered the intrinsic difficulty of semilocal and hybrid functionals for such a purpose. In the context of the XYG3-type double hybrid (xDH) framework, we showed that this particular challenge could be addressed by the top rung functionals only when the many-body nondynamic correlation effect was accounted for adequately. A recently proposed xDH, namely scsRPA, showed unprecedented accuracy with only 5% error on average. The predicted kinetic selectivity by scsRPA is in close agreement with the reference value, revealing a unique versatility of the top rung DFAs for a reliable description of catalytic selectivity of hydrogenation reactions.

催化选择性的准确描述对理论模拟提出了巨大的挑战。由于缺乏关于催化选择性的明确定义的基准，因此即使是广泛使用的密度泛函近似值（DFA）的性能也尚未得到验证。这项工作报告了一套基于氢化钌（Ru）络合物催化的α，β-不饱和醛选择性加氢的测试装置。要特别注意基准醛对不饱和醇或饱和醛的区域选择性。完全基于设置的限制，通过大规模并行实施耦合群集单次，二次和微扰三次激发[CCSD（T）]方法，可以计算出准确的参考数据。此外，我们基于CCSD（T）能量分布图进行了微动力学模拟，为DFA的性能对氢化反应的催化选择性提供最直接的标准。使用此测试集，我们发现了用于此目的的半本地和混合功能的固有困难。在XYG3型双重杂交（xDH）框架的背景下，我们表明只有在充分考虑了多体非动力相关效应的情况下，才能通过顶部梯级功能解决这一特殊挑战。最近提出的xDH，即scsRPA，显示出前所未有的准确性，平均错误率仅为5％。scsRPA预测的动力学选择性与参考值非常吻合，揭示了顶部梯级DFA的独特多功能性，可可靠地描述氢化反应的催化选择性。