G4（MP2）模型、相关一致合成方法和流行密度泛函近似在生成焓基准数据集上的临界基准
Critical Benchmarking of the G4(MP2) Model, the Correlation Consistent Composite Approach and Popular Density Functional Approximations on a Probabilistically Pruned Benchmark Dataset of Formation Enthalpies
摘要：
在化学空间探索所要求的大尺度下，标准生成焓$ΔH_f^\circ$（298 K）的第一性原理计算仅适用于密度泛函近似（DFA）和某些复合波函数理论（cWFT）。唉，流行的范围分离的混合动力，'rung-4' DFA和cWFT的准确性，提供最好的准确性与。成本权衡仅针对主要包括小分子的数据集建立，因此，它们到较大数据集的可转移性仍然模糊。在这项研究中，我们提出了一个扩展的基准数据集超过1600值的$ΔH_f^\circ$的结构和电子多样的分子。我们应用基于边界校正核密度估计的四分位数排序来过滤离群值，并得到1694种化合物的概率修剪焓（PPE 1694）。对于这个数据集，我们使用常规和概率误差指标对G4、G4（MP2）、ccCA、CBS-QB 3和23种流行DFA的预测准确性进行了排名。我们讨论了系统性的预测误差，并强调了经验高阶修正（HLC）在G4（MP2）模型中的作用。此外，我们评论的不确定性与原子的参考经验数据和系统误差从这些增长与分子大小。我们相信这些发现有助于确定量子热化学方法的有意义的应用领域。
英文摘要：
First-principles calculation of the standard formation enthalpy, $ΔH_f^\circ$ (298K), in such large scale as required by chemical space explorations, is amenable only with density functional approximations (DFAs) and some composite wave function theories (cWFTs). Alas, the accuracies of popular range-separated hybrid, `rung-4' DFAs, and cWFTs that offer the best accuracy-vs.-cost trade-off have as yet been established only for datasets predominantly comprising small molecules, hence, their transferability to larger datasets remains vague. In this study, we present an extended benchmark dataset of over 1600 values of $ΔH_f^\circ$ for structurally and electronically diverse molecules. We apply quartile-ranking based on boundary-corrected kernel density estimation to filter outliers and arrive at Probabilistically Pruned Enthalpies of 1694 compounds (PPE1694). For this dataset, we rank the prediction accuracies of G4, G4(MP2), ccCA, CBS-QB3 and 23 popular DFAs using conventional and probabilistic error metrics. We discuss systematic prediction errors and highlight the role an empirical higher-level correction (HLC) plays in the G4(MP2) model. Furthermore, we comment on uncertainties associated with the reference empirical data for atoms and the systematic errors stemming from these that grow with the molecular size. We believe these findings to aid in identifying meaningful application domains for quantum thermochemical methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
