配体(生物化学)
位阻效应
单变量
化学
磷化氢
代表(政治)
反应性(心理学)
工作流程
催化作用
计算化学
计算机科学
组合化学
立体化学
机器学习
有机化学
多元统计
数据库
医学
政治学
生物化学
病理
受体
政治
法学
替代医学
作者
Samuel H. Newman-Stonebraker,Sleight R. Smith,Julia E. Borowski,Ellyn Peters,Tobias Gensch,Heather C. Johnson,Matthew S. Sigman,Abigail G. Doyle
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2021-10-14
卷期号:374 (6565): 301-308
被引量:238
标识
DOI:10.1126/science.abj4213
摘要
Chemists often use statistical analysis of reaction data with molecular descriptors to identify structure-reactivity relationships, which can enable prediction and mechanistic understanding. In this study, we developed a broadly applicable and quantitative classification workflow that identifies reactivity cliffs in 11 Ni- and Pd-catalyzed cross-coupling datasets using monodentate phosphine ligands. A distinctive ligand steric descriptor, minimum percent buried volume [%Vbur (min)], is found to divide these datasets into active and inactive regions at a similar threshold value. Organometallic studies demonstrate that this threshold corresponds to the binary outcome of bisligated versus monoligated metal and that %Vbur (min) is a physically meaningful and predictive representation of ligand structure in catalysis.
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