{"title":"Harnessing Electro-Descriptors for Mechanistic and Machine Learning Analysis of Photocatalytic Organic Reactions","authors":"Luhan Dai, Yulong Fu, Mengran Wei, Fangyuan Wang, Bailin Tian, Guoqiang Wang*, Shuhua Li* and Mengning Ding*, ","doi":"10.1021/jacs.4c03085","DOIUrl":null,"url":null,"abstract":"<p >Photocatalysis has emerged as an effective tool for addressing the contemporary challenges in organic synthesis. However, the trial-and-error-based screening of feasible substrates and optimal reaction conditions remains time-consuming and potentially expensive in industrial practice. Here, we demonstrate an electrochemical-based data-acquisition approach that derives a simple set of redox-relevant electro-descriptors for effective mechanistic analysis and performance evaluation through machine learning (ML) in photocatalytic synthesis. These electro-descriptors correlate to the quantification of shifted charge transfer processes in response to the photoirradiation and enabled construction of reactivity diagram where high-yield reactive “hot zones” can reflect subtle changes of the reaction system. For the model reaction of photocatalytic deoxygenation reaction, the influence of varying carboxylic acids (substrate A, oxidation-intended) and alkenes (substrate B, reduction-intended) and varying reaction conditions on the reaction yield can be visualized, while mathematical analysis of the electro-descriptor patterns further revealed distinct mechanistic/kinetic impacts from different substrates and conditions. Additionally, in the application of ML algorithms, the experimentally derived electro-descriptors reflect an overall redox kinetic outcome contributed from vast reaction parameters, serving as a capable means to reduce the dimensionality in the case of complex multiparameter chemical space. As a result, utilization of electro-descriptors enabled efficient and robust quantitative evaluation of chemical reactivity, demonstrating promising potential of introducing operando-relevant experimental insights in the data-driven chemistry.</p>","PeriodicalId":49,"journal":{"name":"Journal of the American Chemical Society","volume":null,"pages":null},"PeriodicalIF":14.4000,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Chemical Society","FirstCategoryId":"92","ListUrlMain":"https://pubs.acs.org/doi/10.1021/jacs.4c03085","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Photocatalysis has emerged as an effective tool for addressing the contemporary challenges in organic synthesis. However, the trial-and-error-based screening of feasible substrates and optimal reaction conditions remains time-consuming and potentially expensive in industrial practice. Here, we demonstrate an electrochemical-based data-acquisition approach that derives a simple set of redox-relevant electro-descriptors for effective mechanistic analysis and performance evaluation through machine learning (ML) in photocatalytic synthesis. These electro-descriptors correlate to the quantification of shifted charge transfer processes in response to the photoirradiation and enabled construction of reactivity diagram where high-yield reactive “hot zones” can reflect subtle changes of the reaction system. For the model reaction of photocatalytic deoxygenation reaction, the influence of varying carboxylic acids (substrate A, oxidation-intended) and alkenes (substrate B, reduction-intended) and varying reaction conditions on the reaction yield can be visualized, while mathematical analysis of the electro-descriptor patterns further revealed distinct mechanistic/kinetic impacts from different substrates and conditions. Additionally, in the application of ML algorithms, the experimentally derived electro-descriptors reflect an overall redox kinetic outcome contributed from vast reaction parameters, serving as a capable means to reduce the dimensionality in the case of complex multiparameter chemical space. As a result, utilization of electro-descriptors enabled efficient and robust quantitative evaluation of chemical reactivity, demonstrating promising potential of introducing operando-relevant experimental insights in the data-driven chemistry.
期刊介绍:
The flagship journal of the American Chemical Society, known as the Journal of the American Chemical Society (JACS), has been a prestigious publication since its establishment in 1879. It holds a preeminent position in the field of chemistry and related interdisciplinary sciences. JACS is committed to disseminating cutting-edge research papers, covering a wide range of topics, and encompasses approximately 19,000 pages of Articles, Communications, and Perspectives annually. With a weekly publication frequency, JACS plays a vital role in advancing the field of chemistry by providing essential research.