Monitoring C-C coupling in catalytic reactions via machine-learned infrared spectroscopy

作     者：Li Yang Zhicheng Zhao Tongtong Yang Donglai Zhou Xiaoyu Yue Xiyu Li Yan Huang Xijun Wang Ruyun Zheng Thomas Heine Changyin Sun Jun Jiang Sheng Ye Li Yang;Zhicheng Zhao;Tongtong Yang;Donglai Zhou;Xiaoyu Yue;Xiyu Li;Yan Huang;Xijun Wang;Ruyun Zheng;Thomas Heine;Changyin Sun;Jun Jiang;Sheng Ye

作者机构：Institutes of Physical Science and Information Technology School of Chemistry&Chemical EngineeringEngineering Research Center of Autonomous Unmanned System Technology(Ministry of Education) Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology School of Artificial IntelligenceAnhui University Helmholtz-Zentrum Dresden-Rossendorf Theoretical Chemistry Technische Universit?t Dresden Key Laboratory of Precision and Intelligent ChemistrySchool of Chemistry and Materials Science University of Science and Technology of China 

出 版 物：《National Science Review》 (国家科学评论(英文版))

年 卷 期：2025年第12卷第2期

页      面：177-185页

核心收录：

学科分类：12[管理学] 081704[工学-应用化学] 07[理学] 08[工学] 070302[理学-分析化学] 070303[理学-有机化学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 0817[工学-化学工程与技术] 0835[工学-软件工程] 0703[理学-化学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China (21803063, 22003060, 22203001, 22025304,22033007, 62236002, 61921004 and 62306005) the Natural Science Foundation of Anhui Province (2308085MB47,2208085Y04 and 2208085QF192) Anhui Provincial Department of Education university natural science research project(2024AH050070) the Innovation Program for Quantum Science and Technology (2021ZD0303303) the CAS Project for Young Scientists in Basic Research (YSBR-005) the University Synergy Innovation Program of Anhui Province (GXXT-2022-062) Sol2H2 project of the European Union’s Horizon 2020 research and innovation programme (101031846) 

主　　题：chemical evolution machine learning spectroscopic descriptor dynamic monitoring catalytic reaction 

摘      要：Tracking atomic structural evolution along chemical transformation pathways is essential for optimizing chemical transitions and enhancing control. However, molecule-level knowledge of structural rearrangements during chemical processes remains a great challenge. Here, we couple infrared spectroscopy as a non-invasive method to probe molecular transformations, with a machine-learned protocol to immediately map the spectroscopic fingerprints to atomistic structures. From the theoretical perspective, we demonstrate it here with the example of C–C coupling in catalytic reactions, elucidating various structural conformations along dynamic trajectories. Within the transferable application to the specific CO–CO dimerization reaction, the structural and energetic variations of the critical chemical species could be identified via infrared spectroscopy. This approach extends the power of spectroscopy from fingerprinting chemical configurations to using them for assigning dynamic structural information.