Recognizing the critical role of electrolyte chemistry and electrode interfaces in the performance and safety of lithium batteries,along with the urgent need for more sophisticated methods of analysis,this comprehensi...Recognizing the critical role of electrolyte chemistry and electrode interfaces in the performance and safety of lithium batteries,along with the urgent need for more sophisticated methods of analysis,this comprehensive review underscores the promise of machine learning(ML)models in this research field.It explores the application of these innovative methods to studying battery interfaces,particularly focusing on lithium metal anodes.Amid the limitations of traditional experimental techniques,the review supports a hybrid approach that couples experimental and simulation methods,enabling granular insights into the formation process and characteristics of battery interfaces at the molecular level and harnessing AI to extract patterns from voluminous data sets.It showcases the utility of such techniques in electrolyte design and battery life prediction and introduces a novel perspective on battery interface mechanisms.The review concludes by asserting the potential of artificial intelligence(AI)or ML models as invaluable tools in the future of battery research and highlights the importance of fostering confidence in these technologies within the scientific community.展开更多
基金Tao Cheng thanks the support from Suzhou Key Laboratory of Functional Nano&Soft Materials,Collaborative Innovation Center of Suzhou Nano Science&Technology,the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the 111 Project+2 种基金the National Natural Science Foundation of China(No.21903058 and No.22173066)the Natural Science Foundation of Jiangsu Province(BK20190810)William A.GoddardⅢis supported by the Liquid Sunlight Alliance,which is supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,Fuels from Sunlight Hub under Award Number DE-SC0021266.
基金supported by the National Key Research and Development Program of China(2022YFA1504102)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0450302)+6 种基金the National Natural Science Foundation of China(52225105,22279127,52072358 and U21A2082)support from Suzhou Key Laboratory of Functional Nano&Soft Materialsthe Collaborative Innovation Center of Suzhou Nano Science&Technologythe Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the Joint International Research Laboratory of Carbon-Based Functional Materials and Devices(the 111 Project)the National Natural Science Foundation of China(22173066)the National Key Research and Development Program of China(2022YFB2502200)
文摘Recognizing the critical role of electrolyte chemistry and electrode interfaces in the performance and safety of lithium batteries,along with the urgent need for more sophisticated methods of analysis,this comprehensive review underscores the promise of machine learning(ML)models in this research field.It explores the application of these innovative methods to studying battery interfaces,particularly focusing on lithium metal anodes.Amid the limitations of traditional experimental techniques,the review supports a hybrid approach that couples experimental and simulation methods,enabling granular insights into the formation process and characteristics of battery interfaces at the molecular level and harnessing AI to extract patterns from voluminous data sets.It showcases the utility of such techniques in electrolyte design and battery life prediction and introduces a novel perspective on battery interface mechanisms.The review concludes by asserting the potential of artificial intelligence(AI)or ML models as invaluable tools in the future of battery research and highlights the importance of fostering confidence in these technologies within the scientific community.
