Ma Sicong, Cao Yanwei, Shi Yun-Fei, Shang Cheng, He Lin, Liu Zhi-Pan
State Key Laboratory of Metal Organic Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences Shanghai 200032 China
State Key Laboratory for Oxo Synthesis and Selective Oxidation, Lanzhou Institute of Chemical Physics (LICP), Chinese Academy of Sciences Lanzhou 730000 China
Chem Sci. 2024 Jul 19;15(33):13359-13368. doi: 10.1039/d4sc02327g. eCollection 2024 Aug 22.
The design of highly active catalysts is a main theme in organic chemistry, but it still relies heavily on expert experience. Herein, powered by machine-learning global structure exploration, we forge a Metal-Phosphine Catalyst Database (MPCD) with a meticulously designed ligand replacement energy metric, a key descriptor to describe the metal-ligand interactions. It pushes the rational design of organometallic catalysts to a quantitative era, where a ±10 kJ mol window of relative ligand binding strength, a so-called active ligand space (ALS), is identified for highly effective catalyst screening. We highlight the chemistry interpretability and effectiveness of ALS for various C-N, C-C and C-S cross-coupling reactions a Sabatier-principle-based volcano plot and demonstrate its predictive power in discovering low-cost ligands in catalyzing Suzuki cross-coupling involving aryl chloride. The advent of the MPCD provides a data-driven new route for speeding up organometallic catalysis and other applications.
高活性催化剂的设计是有机化学的一个主要主题，但它仍然严重依赖专家经验。在此，在机器学习全局结构探索的推动下，我们构建了一个金属-膦催化剂数据库（MPCD），并精心设计了配体取代能量度量，这是描述金属-配体相互作用的关键描述符。它将有机金属催化剂的合理设计推进到了一个定量时代，在这个时代，为高效催化剂筛选确定了相对配体结合强度的±10 kJ mol窗口，即所谓的活性配体空间（ALS）。我们通过基于萨巴蒂尔原理的火山图突出了ALS对各种碳-氮、碳-碳和碳-硫交叉偶联反应的化学可解释性和有效性，并展示了其在发现催化涉及芳基氯的铃木交叉偶联反应中的低成本配体方面的预测能力。MPCD的出现为加速有机金属催化及其他应用提供了一条数据驱动的新途径。
