Robot-assisted mapping of chemical reaction hyperspaces and networks

IF 48.5 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Nature Pub Date : 2025-09-24 DOI:10.1038/s41586-025-09490-1
Yankai Jia, Rafał Frydrych, Yaroslav I. Sobolev, Wai-Shing Wong, Bibek Prajapati, Daniel Matuszczyk, Yasemin Bilgi, Louis Gadina, Juan Carlos Ahumada, Galymzhan Moldagulov, Namhun Kim, Eric S. Larsen, Maxence Deschamps, Yanqiu Jiang, Bartosz A. Grzybowski
{"title":"Robot-assisted mapping of chemical reaction hyperspaces and networks","authors":"Yankai Jia, Rafał Frydrych, Yaroslav I. Sobolev, Wai-Shing Wong, Bibek Prajapati, Daniel Matuszczyk, Yasemin Bilgi, Louis Gadina, Juan Carlos Ahumada, Galymzhan Moldagulov, Namhun Kim, Eric S. Larsen, Maxence Deschamps, Yanqiu Jiang, Bartosz A. Grzybowski","doi":"10.1038/s41586-025-09490-1","DOIUrl":null,"url":null,"abstract":"Despite decades of investigation, it remains unclear (and hard to predict1–4) how the outcomes of chemical reactions change over multidimensional ‘hyperspaces’ defined by reaction conditions5. Whereas human chemists can explore only a limited subset of these manifolds, automated platforms6–12 can generate thousands of reactions in parallel. Yet, purification and yield quantification remain bottlenecks, constrained by time-consuming and resource-intensive analytical techniques. As a result, our understanding of reaction hyperspaces remains fragmentary7,9,13–16. Are yield distributions smooth or corrugated? Do they conceal mechanistically new reactions? Can major products vary across different regions? Here, to address these questions, we developed a low-cost robotic platform using primarily optical detection to quantify yields of products and by-products at unprecedented throughput and minimal cost per condition. Scanning hyperspaces across thousands of conditions, we find and prove mathematically that, for continuous variables (concentrations, temperatures), individual yield distributions are generally slow-varying. At the same time, we uncover hyperspace regions of unexpected reactivity as well as switchovers between major products. Moreover, by systematically surveying substrate proportions, we reconstruct underlying reaction networks and expose hidden intermediates and products—even in reactions studied for well over a century. This hyperspace-scanning approach provides a versatile and scalable framework for reaction optimization and discovery. Crucially, it can help identify conditions under which complex mixtures can be driven cleanly towards different major products, thereby expanding synthetic diversity while reducing chemical input requirements. A low-cost robotic platform using mainly optical detection to quantify yields of products and by-products allows the analysis of multidimensional chemical reaction hyperspaces and networks much faster than is possible by human chemists.","PeriodicalId":18787,"journal":{"name":"Nature","volume":"645 8082","pages":"922-931"},"PeriodicalIF":48.5000,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41586-025-09490-1.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature","FirstCategoryId":"103","ListUrlMain":"https://www.nature.com/articles/s41586-025-09490-1","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Despite decades of investigation, it remains unclear (and hard to predict1–4) how the outcomes of chemical reactions change over multidimensional ‘hyperspaces’ defined by reaction conditions5. Whereas human chemists can explore only a limited subset of these manifolds, automated platforms6–12 can generate thousands of reactions in parallel. Yet, purification and yield quantification remain bottlenecks, constrained by time-consuming and resource-intensive analytical techniques. As a result, our understanding of reaction hyperspaces remains fragmentary7,9,13–16. Are yield distributions smooth or corrugated? Do they conceal mechanistically new reactions? Can major products vary across different regions? Here, to address these questions, we developed a low-cost robotic platform using primarily optical detection to quantify yields of products and by-products at unprecedented throughput and minimal cost per condition. Scanning hyperspaces across thousands of conditions, we find and prove mathematically that, for continuous variables (concentrations, temperatures), individual yield distributions are generally slow-varying. At the same time, we uncover hyperspace regions of unexpected reactivity as well as switchovers between major products. Moreover, by systematically surveying substrate proportions, we reconstruct underlying reaction networks and expose hidden intermediates and products—even in reactions studied for well over a century. This hyperspace-scanning approach provides a versatile and scalable framework for reaction optimization and discovery. Crucially, it can help identify conditions under which complex mixtures can be driven cleanly towards different major products, thereby expanding synthetic diversity while reducing chemical input requirements. A low-cost robotic platform using mainly optical detection to quantify yields of products and by-products allows the analysis of multidimensional chemical reaction hyperspaces and networks much faster than is possible by human chemists.

Abstract Image

化学反应超空间和网络的机器人辅助映射
尽管经过了几十年的研究,化学反应的结果如何在由反应条件定义的多维“超空间”中发生变化仍然不清楚(也很难预测)。人类化学家只能探索这些流形的有限子集,而自动化平台可以同时产生数千种反应。然而,纯化和产量定量仍然是瓶颈,受到耗时和资源密集型分析技术的限制。因此,我们对反应超空间的理解仍然是零碎的7,9,13 - 16。产量分布是平滑的还是波纹状的?它们是否隐藏了机械性的新反应?不同地区的主要产品会有所不同吗?在这里,为了解决这些问题,我们开发了一个低成本的机器人平台,主要使用光学检测来量化产品和副产品的产量,以前所未有的吞吐量和最低的成本。通过对数千种条件下的超空间进行扫描,我们发现并从数学上证明,对于连续变量(浓度、温度),单个产量分布通常是缓慢变化的。与此同时,我们发现了意想不到的反应性的超空间区域以及主要产品之间的切换。此外,通过系统地调查底物比例,我们重建了潜在的反应网络,并揭示了隐藏的中间体和产物——即使在研究了一个多世纪的反应中也是如此。这种超空间扫描方法为反应优化和发现提供了一个通用的、可扩展的框架。至关重要的是,它可以帮助确定复杂混合物可以清洁地用于不同主要产品的条件,从而扩大合成多样性,同时减少化学投入要求。一个低成本的机器人平台,主要使用光学检测来量化产品和副产物的产量,可以比人类化学家更快地分析多维化学反应超空间和网络。
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
Nature
Nature 综合性期刊-综合性期刊
CiteScore
90.00
自引率
1.20%
发文量
3652
审稿时长
3 months
期刊介绍: Nature is a prestigious international journal that publishes peer-reviewed research in various scientific and technological fields. The selection of articles is based on criteria such as originality, importance, interdisciplinary relevance, timeliness, accessibility, elegance, and surprising conclusions. In addition to showcasing significant scientific advances, Nature delivers rapid, authoritative, insightful news, and interpretation of current and upcoming trends impacting science, scientists, and the broader public. The journal serves a dual purpose: firstly, to promptly share noteworthy scientific advances and foster discussions among scientists, and secondly, to ensure the swift dissemination of scientific results globally, emphasizing their significance for knowledge, culture, and daily life.
×
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:604180095
Book学术官方微信