{"title":"High-Throughput Optimization of a High-Pressure Catalytic Reaction.","authors":"Yusuke Tanabe, Hiroki Sugisawa, Tomohisa Miyazawa, Kazuhiro Hotta, Kazuya Shiratori, Tadahiro Fujitani","doi":"10.1021/acs.jcim.4c02273","DOIUrl":null,"url":null,"abstract":"<p><p>High-throughput optimization of a hydroformylation reaction using CO<sub>2</sub> instead of CO was performed through Bayesian optimization in combination with a high-throughput screening system. CO<sub>2</sub> and H<sub>2</sub> pressure as well as catalyst composition were efficiently optimized by transferring a surrogate model, constructed through catalyst composition optimization, for the comprehensive optimization of the entire search space. This method successfully increased the aldehyde yield by 1.5 times compared to that reported in the literature with a combination of small amounts of Rh and Ru catalysts combined with ionic liquid with chloride ions. The optimization was completed within 1-2 months through the combination of AI, robotics, and human expertise, demonstrating the feasibility of rapid catalyst development, even for high-pressure reactions.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6000,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Information and Modeling ","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acs.jcim.4c02273","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 0
Abstract
High-throughput optimization of a hydroformylation reaction using CO2 instead of CO was performed through Bayesian optimization in combination with a high-throughput screening system. CO2 and H2 pressure as well as catalyst composition were efficiently optimized by transferring a surrogate model, constructed through catalyst composition optimization, for the comprehensive optimization of the entire search space. This method successfully increased the aldehyde yield by 1.5 times compared to that reported in the literature with a combination of small amounts of Rh and Ru catalysts combined with ionic liquid with chloride ions. The optimization was completed within 1-2 months through the combination of AI, robotics, and human expertise, demonstrating the feasibility of rapid catalyst development, even for high-pressure reactions.
期刊介绍：
The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery.
Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field.
As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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