Automated approaches, reaction parameterisation, and data science in organometallic chemistry and catalysis: towards improving synthetic chemistry and accelerating mechanistic understanding

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
Stuart C. Smith, Christopher S. Horbaczewskyj, Theo F. N. Tanner, Jacob J. Walder and Ian J. S. Fairlamb
{"title":"Automated approaches, reaction parameterisation, and data science in organometallic chemistry and catalysis: towards improving synthetic chemistry and accelerating mechanistic understanding","authors":"Stuart C. Smith, Christopher S. Horbaczewskyj, Theo F. N. Tanner, Jacob J. Walder and Ian J. S. Fairlamb","doi":"10.1039/D3DD00249G","DOIUrl":null,"url":null,"abstract":"<p >Automation technologies and data science techniques have been successfully applied to optimisation and discovery activities in the chemical sciences for decades. As the sophistication of these techniques and technologies have evolved, so too has the ambition to expand their scope of application to problems of significant synthetic difficulty. Of these applications, some of the most challenging involve investigation of chemical mechanism in organometallic processes (with particular emphasis on air- and moisture-sensitive processes), particularly with the reagent and/or catalyst used. We discuss herein the development of enabling methodologies to allow the study of these challenging systems and highlight some important applications of these technologies in problems of considerable interest to applied synthetic chemists.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/dd/d3dd00249g?page=search","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital discovery","FirstCategoryId":"1085","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2024/dd/d3dd00249g","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Automation technologies and data science techniques have been successfully applied to optimisation and discovery activities in the chemical sciences for decades. As the sophistication of these techniques and technologies have evolved, so too has the ambition to expand their scope of application to problems of significant synthetic difficulty. Of these applications, some of the most challenging involve investigation of chemical mechanism in organometallic processes (with particular emphasis on air- and moisture-sensitive processes), particularly with the reagent and/or catalyst used. We discuss herein the development of enabling methodologies to allow the study of these challenging systems and highlight some important applications of these technologies in problems of considerable interest to applied synthetic chemists.

Abstract Image

Abstract Image

有机金属化学和催化中的自动化方法、反应参数化和数据科学:改善合成化学并加速机理理解
几十年来,自动化技术和数据科学技术已成功应用于化学科学领域的优化和发现活动。随着这些技术和工艺的不断发展,人们也希望扩大其应用范围,以解决具有重大合成难度的问题。在这些应用中,一些最具挑战性的应用涉及有机金属过程(特别强调对空气和湿气敏感的过程)中化学机制的研究,尤其是所使用的试剂和/或催化剂。我们将在本文中讨论为研究这些具有挑战性的系统而开发的有利方法,并重点介绍这些技术在应用合成化学家相当感兴趣的问题中的一些重要应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
2.80
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信