Hueffel Julian A, Rigoulet Mathilde, Wellig Sebastian, Sperger Theresa, Ward Jas S, Rissanen Kari, Schoenebeck Franziska
Institute of Organic Chemistry, RWTH Aachen University, Landoltweg 1, 52074 Aachen, Germany.
University of Jyvaskyla, Department of Chemistry, FIN40014 Jyväskylä, Finland.
J Am Chem Soc. 2025 Jul 30;147(30):26149-26157. doi: 10.1021/jacs.5c00441. Epub 2025 Jul 18.
The fate of a catalyst or catalytic intermediate, i.e., its speciation, is a critical aspect of the efficiency of a catalyst as well as the overall reactivity and selectivity of the catalyzed transformation. However, the precise factors that dictate catalyst speciation are rarely understood and trial-and-error approaches frequently prevail. To address this challenge and develop predictive tools to guide ligand selection for a desired metal speciation in a catalytically relevant context, we evaluated the feasibility of machine learning combined with computational activation barrier predictions to achieve CO insertion at room temperature for the sterically least hindered (and most vulnerable) Ni-Ph complexes, which constitute key catalytic intermediates. Following an in depth computational rationalization on the origin of reactivity difference of Ni versus Ni toward CO insertion, we subsequently pursued machine learning to identify ligands that favor the critical Ni oxidation state. To this end, a descriptor database was constructed . Subsequent application of machine learning led to the prediction of numerous ligands that favor the more reactive Ni-Ph intermediate and oxidation state, which were subsequently filtered for candidates that also show desired room temperature reactivity through the calculation of activation barriers. Ultimately, a set of representative candidates was synthesized and experimentally tested for CO insertion, confirming their reactivity and alignment with computational predictions. This work offers a blueprint for creating and analyzing virtual databases to predict ligands-including never synthesized ones-that control metal complex oxidation state, nuclearity, and reactivity.
催化剂或催化中间体的命运，即其形态，是催化剂效率以及催化转化的整体反应性和选择性的关键方面。然而，决定催化剂形态的精确因素很少被理解，试错法经常占主导地位。为了应对这一挑战并开发预测工具，以在催化相关背景下指导配体选择以实现所需的金属形态，我们评估了机器学习与计算活化能垒预测相结合的可行性，以实现空间位阻最小（也是最易受影响）的Ni-Ph配合物在室温下的CO插入反应，这些配合物是关键的催化中间体。在对Ni与Ni对CO插入反应性差异的起源进行深入的计算合理化分析之后，我们随后采用机器学习来识别有利于关键Ni氧化态的配体。为此，构建了一个描述符数据库。随后应用机器学习预测了许多有利于更具反应性的Ni-Ph中间体和氧化态的配体，随后通过计算活化能垒对这些配体进行筛选，以找出也具有所需室温反应性的候选配体。最终，合成了一组代表性的候选配体并对其进行了CO插入反应的实验测试，证实了它们的反应性以及与计算预测结果的一致性。这项工作为创建和分析虚拟数据库提供了一个蓝图，以预测控制金属配合物氧化态、核数和反应性的配体，包括从未合成过的配体。
