Predicting hydroformylation regioselectivity from literature data via machine learning

被引：0

作者：

Chen, Shuai ^{[1
]}

Pollice, Robert ^{[1
]}

机构：

[1] Stratingh Inst Chem, Groningen, Netherlands

来源：

CHEM CATALYSIS | 2024年 / 4卷 / 09期

关键词：

D O I：

10.1016/j.checat.2024.101111

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

In this issue of Chem Catalysis, Mao et al. develop machine learning models that predict terminal alkene regioselectivity in catalytic hydroformylation, showing that high temperature, low pressure, and low metal concentration favor linear products. These models enable high-throughput screening, potentially advancing innovations in this industrial process.

引用

页数：3

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