Exploring the production and application of clean energy has always been the core of sustainable development.As a clean and sustainable technology,electrocatalysis has been receiving widespread attention.It is crucial...Exploring the production and application of clean energy has always been the core of sustainable development.As a clean and sustainable technology,electrocatalysis has been receiving widespread attention.It is crucial to achieve efficient,stable and cheap electrocatalysts.However,the traditional“trial and error”method is time-consuming,laborious and costly.In recent years,with the significant increase in computing power,computations have played an important role in electrocatalyst design.Nevertheless,it is still difficult to search for advanced electrocatalysts in the vast chemical space through traditional density functional theory(DFT)computations.Fortunately,the development of machine learning and interdisciplinary integration will inject new impetus into targeted design of electrocatalysts.Machine learning is able to predict electrochemical performances with an accuracy close to DFT.Here we provide an overview of the application of machine learning in electrocatalyst design,including the prediction of structure,thermodynamic properties and kinetic barriers.We also discuss the potential of explicit solvent model combined with machine learning molecular dynamics in this field.Finally,the favorable circumstances and challenges are outlined for the future development of machine learning in electrocatalysis.The studies on electrochemical processes by machine learning will further realize targeted design of high-efficiency electrocatalysts.展开更多
Mass transfer and catalyst recovery are two crucial issues in solid base catalysis,while the cumbersome operation steps and the associated time and energy penalties are still inevitable for conventional catalysts.Achi...Mass transfer and catalyst recovery are two crucial issues in solid base catalysis,while the cumbersome operation steps and the associated time and energy penalties are still inevitable for conventional catalysts.Achieving the technical upgrades through catalyst design is desirable but challenging because of the difficulty in satisfying diverse demands of different steps.In this work,a magnetically responsive solid base catalyst with the rod-like nanostructure was developed.The rod-like solid base catalysts are composed of Fe_(3)O_(4) cores,silica shells and calcium oxide active sites.The functions of magnetic recovery and stirring were integrated into the catalyst,which applies in both the general catalytic processes and microchannel reactors given their nanoscales.When applied to the synthesis of dimethyl carbonate by onestep transesterification of methanol and ethylene carbonate,an apparent enhancement on turnover frequency value(33.1 h^(−1))was observed for nano-stirring compared with that tested without stirring(12.1 h^(−1))within 30 min.The present catalysts may open up new avenues in the development of advanced solid base catalysts.展开更多
文摘Exploring the production and application of clean energy has always been the core of sustainable development.As a clean and sustainable technology,electrocatalysis has been receiving widespread attention.It is crucial to achieve efficient,stable and cheap electrocatalysts.However,the traditional“trial and error”method is time-consuming,laborious and costly.In recent years,with the significant increase in computing power,computations have played an important role in electrocatalyst design.Nevertheless,it is still difficult to search for advanced electrocatalysts in the vast chemical space through traditional density functional theory(DFT)computations.Fortunately,the development of machine learning and interdisciplinary integration will inject new impetus into targeted design of electrocatalysts.Machine learning is able to predict electrochemical performances with an accuracy close to DFT.Here we provide an overview of the application of machine learning in electrocatalyst design,including the prediction of structure,thermodynamic properties and kinetic barriers.We also discuss the potential of explicit solvent model combined with machine learning molecular dynamics in this field.Finally,the favorable circumstances and challenges are outlined for the future development of machine learning in electrocatalysis.The studies on electrochemical processes by machine learning will further realize targeted design of high-efficiency electrocatalysts.
基金supported by the National Natural Science Foundation of China Youth Project(21808110)the financial support of this work by the National Science Fund for Distinguished Young Scholars(22125804)the National Natural Science Foundation of China(21878149,22078155,and 21722606)。
文摘Mass transfer and catalyst recovery are two crucial issues in solid base catalysis,while the cumbersome operation steps and the associated time and energy penalties are still inevitable for conventional catalysts.Achieving the technical upgrades through catalyst design is desirable but challenging because of the difficulty in satisfying diverse demands of different steps.In this work,a magnetically responsive solid base catalyst with the rod-like nanostructure was developed.The rod-like solid base catalysts are composed of Fe_(3)O_(4) cores,silica shells and calcium oxide active sites.The functions of magnetic recovery and stirring were integrated into the catalyst,which applies in both the general catalytic processes and microchannel reactors given their nanoscales.When applied to the synthesis of dimethyl carbonate by onestep transesterification of methanol and ethylene carbonate,an apparent enhancement on turnover frequency value(33.1 h^(−1))was observed for nano-stirring compared with that tested without stirring(12.1 h^(−1))within 30 min.The present catalysts may open up new avenues in the development of advanced solid base catalysts.
