Deriving intuition in catalyst design with machine learning

被引：3
|
作者：
Rodrigues, Tiago [1 ]
机构：
[1] Univ Lisbon, Fac Farm, Inst Invest Med iMed, Ave Prof Gama Pinto, P-1649003 Lisbon, Portugal
来源：
CHEM | 2022年 / 8卷 / 01期
关键词：
D O I：
10.1016/j.chempr.2021.12.006
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
Catalysts for asymmetric syntheses provide a powerful means to manipulate and obtain matter with defined stereochemistry. In a recent issue of Cell Reports Physical Science, Kanai and colleagues report on a data-driven approach to identify key components in iridium/boron catalysts for the on-demand synthesis of stereochemically defined alpha-allyl carboxylic acids.
引用
收藏
页码：15 / +
页数：3
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