Predicting hydroformylation regioselectivity from literature data via machine learning

IF 11.5 Q1 CHEMISTRY, PHYSICAL Chem Catalysis Pub Date : 2024-09-19 DOI:10.1016/j.checat.2024.101111
Shuai Chen, Robert Pollice
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引用次数: 0

Abstract

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.

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通过机器学习从文献数据中预测加氢转化的选择性
在本期《化学催化》(Chem Catalysis)杂志上,Mao 等人开发了机器学习模型,用于预测催化加氢甲酰化过程中端烯的区域选择性,结果表明高温、低压和低金属浓度有利于线性产物。这些模型实现了高通量筛选,有望推动这一工业过程的创新。
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来源期刊
CiteScore
10.50
自引率
6.40%
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0
期刊介绍: Chem Catalysis is a monthly journal that publishes innovative research on fundamental and applied catalysis, providing a platform for researchers across chemistry, chemical engineering, and related fields. It serves as a premier resource for scientists and engineers in academia and industry, covering heterogeneous, homogeneous, and biocatalysis. Emphasizing transformative methods and technologies, the journal aims to advance understanding, introduce novel catalysts, and connect fundamental insights to real-world applications for societal benefit.
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