Title: Artificial‑Intelligence‑Driven Design of Lithium‑Battery Electrolytes
Speaker: Associate Researcher Xiang Chen (Tsinghua University)
Invited by: Professor Liang Zhenxing
Time: August 2, 2026 (Sunday), 16:00‑17:00
Venue: Room 901, Building B13, University Town Campus
Organizer: School of Chemistry and Chemical Engineering
Welcome all teachers and students to attend!
Issued by School of Chemistry and Chemical EngineeringJuly 30, 2026
Biography:
Xiang Chen is an Associate Researcher at Tsinghua University. His research focuses on fundamental theoretical studies of energy chemistry. He has proposed concepts including lithium‑bond chemistry and ion‑solvent structures, established a world‑leading electrolyte database, developed artificial‑intelligence‑enabled electrolyte design methodologies and intelligent R&D experimental systems, and obtained multiple advanced electrolyte systems. As (co‑)first author or corresponding author, he has published more than 60 SCI papers in journals such as Chem. Rev., Chem. Soc. Rev., Acc. Chem. Res., Sci. Adv., Chem, Angew. Chem., and J. Am. Chem. Soc.. His H‑index is 88, with over 28 000 citations. He was selected for the MIT Technology Review TR35 Asia‑Pacific 2023 list, the inaugural AI100 Young Pioneers, and Clarivate Highly Cited Researchers (2020‑2025). He has undertaken research projects including the Excellent Young Scientists Fund of the National Natural Science Foundation of China, the Cultivation Project of Major Research Plan, Beijing Science and Technology Program, and sub‑projects of the Key R&D Program of the Ministry of Science and Technology. He serves as an independent reviewer for journals including Nature, Nat. Energy, Nat. Chem., and Nat. Catal.; Associate Editor for J. Energy Chem. and Energy Environ. Mater.; Youth Editorial Board Member for Chinese Chem. Lett. and Green Carbon; and Youth Director of the Chinese Society of Particuology.
Abstract:
As one of the core components of lithium‑ion batteries, electrolytes function primarily for lithium‑ion transport and exert a remarkable influence on practical battery performance, figuratively known as the “blood of batteries”. Nevertheless, the design and development of advanced electrolytes are confronted with challenges such as complex solution‑chemistry principles, an enormous pool of candidate electrolyte molecules, and difficulties in optimization originating from strong correlations among electrolyte components. This lecture focuses on understanding the solvent‑chemistry principles of electrolytes and the machine‑learning‑based design of advanced electrolytes. Specifically, multi‑scale simulation approaches including first‑principles calculations and molecular dynamics simulations are adopted to explore solvent‑chemistry rules of electrolytes. It is revealed that the formation of ion‑solvent structures constitutes a critical factor governing interfacial stability of electrolytes. Multiple prediction methods for electrolyte physical properties such as dielectric constant and viscosity have been developed, and a world‑leading large‑scale electrolyte database has been constructed, covering 250 000 molecular structures and more than 20 electrolyte properties. Machine‑learning models and software platforms have been developed to quantitatively correlate molecular structures and physicochemical properties of electrolytes, enabling high‑throughput screening and inverse design of electrolyte molecules, whereby more than ten novel molecular systems have been predicted. A high‑throughput intelligent evaluation chemical‑robot platform for electrolytes has been established to build closed‑loop iteration between artificial‑intelligence‑driven design and experimental characterizations, thus realizing efficient and intelligent R&D of advanced electrolytes. The above‑mentioned large‑scale electrolyte database, novel artificial‑intelligence methodologies, and chemical‑robot platforms enable rapid and accurate design of electrolyte molecules within a chemical space of hundreds of millions of molecules. These advances promote the practical deployment of next‑generation high‑energy‑density batteries and provide pivotal technical support for achieving China’s Dual‑Carbon Goals.
Announced by School of Chemistry and Chemical Engineering