Xiang Chen, Qiang Zhang
Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Department of Chemical
Engineering, Tsinghua University, Beijing 100084, China
EXTENDED ABSTRACT: Electrolyte is one critical component of lithium batteries, which mainly plays the function of transporting lithium ions and is vividly known as "battery blood". More importantly, electrolyte significantly affects the actual performance of batteries. However, the design of advanced electrolytes is faced with grand challenges, such as the complex chemical principles of solution, a large number of electrolyte molecules, and the difficult optimization of electrolyte components due to the strong correlation among them. This report will focus on how AI technology can solve the bottleneck of current advanced electrolyte research and development, including data-driven discovery of electrolyte solvent chemistry, high-throughput computing to build an electrolyte database, machine learning to quantitatively relate electrolyte molecular structure and physical and chemical properties to accelerate the precise design of electrolyte molecules in the space of hundreds of millions of molecules. Therefore, this work provides a potential solution for the next generation of lithium battery electrolyte and provides key technical support for the realization of the carbon neutralizing target.
Keywords: lithium batteries; electrolyte; artificial intelligence; molecular machine learning; big database
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Xiang Chen gained his Bachelor's and Ph.D. degrees from the Department of Chemical Engineering at Tsinghua University in 2016 and 2021, respectively. Then, he was a Shuimu Tsinghua Scholar at Tsinghua University. Currently, he is an assistant professor at Tsinghua University. His research interests focus on understanding the chemical mechanism and materials science in rechargeable batteries mainly through multi-scale simulations and machine learning. He has published more than forty (co)-first-author papers in Chem. Rev., Acc. Chem. Res., Sci. Adv., Chem, Angew. Chem., J. Am. Chem. Soc., J. Energy Chem., Fundam. Res. et al., with more than 14000 citations and an H-index of 57. He has been rewarded the 2023 MIT TR35 Asia Pacific and the Clarivate Highly Cited Researchers from 2020 to 2022. He is supported by the National Natural Science Foundation of China and the Young Elite Scientists Sponsorship Program by CAST. He has reviewed more than 100 papers for high-impact journals such as Nature, Nat. Cata!., Nat. Commun., Angew. Chem., and Joule.