{"title":"Predicting hydroformylation regioselectivity from literature data via machine learning","authors":"Shuai Chen, Robert Pollice","doi":"10.1016/j.checat.2024.101111","DOIUrl":null,"url":null,"abstract":"<p>In this issue of <em>Chem Catalysis</em>, 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.</p>","PeriodicalId":53121,"journal":{"name":"Chem Catalysis","volume":null,"pages":null},"PeriodicalIF":11.5000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chem Catalysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.checat.2024.101111","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Predicting hydroformylation regioselectivity from literature data via machine learning
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.
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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