Department of Chemistry, Columbia University , New York, New York 10027, United States.
Department of Chemistry, Colorado State University , Fort Collins, Colorado 80523, United States.
J Am Chem Soc. 2017 Jan 25;139(3):1296-1310. doi: 10.1021/jacs.6b11670. Epub 2017 Jan 6.
CpRh(III)-catalyzed C-H functionalization reactions are a proven method for the efficient assembly of small molecules. However, rationalization of the effects of cyclopentadienyl (Cp) ligand structure on reaction rate and selectivity has been viewed as a black box, and a truly systematic study is lacking. Consequently, predicting the outcomes of these reactions is challenging because subtle variations in ligand structure can cause notable changes in reaction behavior. A predictive tool is, nonetheless, of considerable value to the community as it would greatly accelerate reaction development. Designing a data set in which the steric and electronic properties of the CpRh(III) catalysts were systematically varied allowed us to apply multivariate linear regression algorithms to establish correlations between these catalyst-based descriptors and the regio-, diastereoselectivity, and rate of model reactions. This, in turn, led to the development of quantitative predictive models that describe catalyst performance. Our newly described cone angles and Sterimol parameters for Cp ligands served as highly correlative steric descriptors in the regression models. Through rational design of training and validation sets, key diastereoselectivity outliers were identified. Computations reveal the origins of the outstanding stereoinduction displayed by these outliers. The results are consistent with partial η-η ligand slippage that occurs in the transition state of the selectivity-determining step. In addition to the instructive value of our study, we believe that the insights gained are transposable to other group 9 transition metals and pave the way toward rational design of C-H functionalization catalysts.
CpRh(III)催化的 C-H 功能化反应是一种有效的小分子高效组装方法。然而,Cp 配体结构对反应速率和选择性影响的合理化一直被视为一个黑箱,缺乏真正系统的研究。因此,预测这些反应的结果具有挑战性,因为配体结构的细微变化可能导致反应行为的显著变化。然而,对于该领域而言,预测工具具有相当大的价值,因为它将大大加快反应的开发。设计了一个数据集,其中系统地改变了 CpRh(III)催化剂的立体和电子性质,使我们能够应用多元线性回归算法来建立这些基于催化剂的描述符与模型反应的区域选择性、非对映选择性和速率之间的相关性。这反过来又导致了描述催化剂性能的定量预测模型的发展。我们新描述的 Cp 配体的锥角和 Sterimol 参数在回归模型中作为高度相关的立体描述符。通过训练集和验证集的合理设计,确定了关键的非对映选择性异常值。计算揭示了这些异常值显示出出色的立体诱导的起源。结果与在决定选择性的步骤的过渡态中发生的部分η-η配体滑移一致。除了我们研究的指导价值外,我们相信所获得的见解可推广到其他第 9 族过渡金属,并为 C-H 功能化催化剂的合理设计铺平道路。