Identifying chemical reaction processes is essential for exploring the mechanism and optimizing the reaction.In situ spectroscopy can provide real-time information on molecules during chemical reactions to help reveal...Identifying chemical reaction processes is essential for exploring the mechanism and optimizing the reaction.In situ spectroscopy can provide real-time information on molecules during chemical reactions to help reveal the reaction mechanism and dynamics.However,it is time-consuming and laborious to observe and decipher the spectra manually,and it is difficult for humans to capture the subtle differences between spectra,which makes it difficult to do quantitative analysis.The noise of the reaction systems also poses a greater challenge.Herein,we employed a combination of machine learning(ML)and spectroscopic techniques to establish a correlation between spectroscopic data and the processes of the carbon dioxide reduction reaction.By intelligently resolving the vibrational spectral signals,we can accurately identify the individual steps of the reaction.With a small amount of additional data,the approach is not only applicable to different systems,but also maintains good performance in noisy environments.Furthermore,we establish the spectrastructure quantitative relationship to obtain more refined reaction coordinates.Identifying chemical reaction processes using such an ML framework lays the foundation for unraveling the black box of chemical reactions between reactants and products,and provides a new strategy for understanding and optimizing chemical reactions.展开更多
基金supported by the National Natural Science Foundation of China(grant nos.22203082,22303091,12227901,22025304,and 22033007)the Fundamental Research Funds for the Central Universities(grant no.WK9990000130)+1 种基金the Innovation Program for Quantum Science and Technology(grant no.2021ZD0303303)Hefei Comprehensive National Science Center.
文摘Identifying chemical reaction processes is essential for exploring the mechanism and optimizing the reaction.In situ spectroscopy can provide real-time information on molecules during chemical reactions to help reveal the reaction mechanism and dynamics.However,it is time-consuming and laborious to observe and decipher the spectra manually,and it is difficult for humans to capture the subtle differences between spectra,which makes it difficult to do quantitative analysis.The noise of the reaction systems also poses a greater challenge.Herein,we employed a combination of machine learning(ML)and spectroscopic techniques to establish a correlation between spectroscopic data and the processes of the carbon dioxide reduction reaction.By intelligently resolving the vibrational spectral signals,we can accurately identify the individual steps of the reaction.With a small amount of additional data,the approach is not only applicable to different systems,but also maintains good performance in noisy environments.Furthermore,we establish the spectrastructure quantitative relationship to obtain more refined reaction coordinates.Identifying chemical reaction processes using such an ML framework lays the foundation for unraveling the black box of chemical reactions between reactants and products,and provides a new strategy for understanding and optimizing chemical reactions.
