Deciphering Coulombic Efficiency of Lithium Metal Anodes by Screening Electrolyte Properties

Z Zhao Zheng (Beijing Key Laboratory of Complex Solid State Batteries & Tsinghua Center for Green Chemical Engineering Electrification, Department of Chemical Engineering) X Xinyan Liu (Institute of Fundamental and Frontier Science) X Xue‐Qiang Zhang (School of Materials Science and Engineering Beijing Institute of Technology Beijing 100081 P.R. China) S Shu‐Yu Sun (Beijing Key Laboratory of Complex Solid State Batteries Department of Chemical Engineering Tsinghua University Beijing 100084 P.R. China) J Jia‐Lin Li (School of Materials Science and Engineering Beijing Institute of Technology Beijing 100081 P.R. China) Y Ya‐Nan Wang (School of Materials Science and Engineering Beijing Institute of Technology Beijing 100081 P.R. China) N Nan Yao D Dong‐Hao Zhan (Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing 100081 P.R. China) W Wen‐Jun Feng (Beijing Key Laboratory of Complex Solid State Batteries Department of Chemical Engineering Tsinghua University Beijing 100084 P.R. China) H Hong‐Jie Peng (Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu Sichuan 611731 P.R. China) J Jiang‐Kui Hu (Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing 100081 P.R. China) J Jia‐Qi Huang (School of Interdisciplinary Science Beijing Institute of Technology Beijing P. R. China) Q Qiang Zhang

Abstract

Abstract Coulombic efficiency (CE) is a quantifiable indicator for the reversibility of lithium metal anodes in high‐energy‐density batteries. However, the quantitative relationship between CE and electrolyte properties has yet to be established, impeding rational electrolyte design. Herein, an interpretable model for estimating CE based on data‐driven insights of electrolyte properties is proposed. Hydrogen‐bond acceptor basicity ( β ) and the energy level gap between the lowest unoccupied and the highest occupied molecular orbital (HOMO‐LUMO gap) of solvents are identified as the top two parameters impacting CE by machine learning. β and HOMO‐LUMO gap of solvents govern anode interphase chemistry. A regression model is further proposed to estimate the CE based on β and HOMO‐LUMO gap. Using the new solvent screened by above regression model, the lithium metal anode in the pouch cell with an energy density of 418 Wh kg −1 achieves the highest CE of 99.2%, which is much larger than previous CE ranging from 70%–98.5%. This work provides a reliable interpretable quantitative model for rational electrolyte design.

Article Details

Volume / Issue Vol. 64, Issue 30
Published July 21, 2025
ISSN 1433-7851
Publisher Wiley

Journal Info

Angewandte Chemie International Edition

Wiley

ISSN: 1433-7851 Physical Sciences

Authors (13)

Z

Zhao Zheng

Beijing Key Laboratory of Complex Solid State Batteries & Tsinghua Center for Green Chemical Engineering Electrification, Department of Chemical Engineering

X

Xinyan Liu

Institute of Fundamental and Frontier Science

X

Xue‐Qiang Zhang

School of Materials Science and Engineering Beijing Institute of Technology Beijing 100081 P.R. China

S

Shu‐Yu Sun

Beijing Key Laboratory of Complex Solid State Batteries Department of Chemical Engineering Tsinghua University Beijing 100084 P.R. China

J

Jia‐Lin Li

School of Materials Science and Engineering Beijing Institute of Technology Beijing 100081 P.R. China

Y

Ya‐Nan Wang

School of Materials Science and Engineering Beijing Institute of Technology Beijing 100081 P.R. China

N

Nan Yao

D

Dong‐Hao Zhan

Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing 100081 P.R. China

W

Wen‐Jun Feng

Beijing Key Laboratory of Complex Solid State Batteries Department of Chemical Engineering Tsinghua University Beijing 100084 P.R. China

H

Hong‐Jie Peng

Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu Sichuan 611731 P.R. China

J

Jiang‐Kui Hu

Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing 100081 P.R. China

J

Jia‐Qi Huang

School of Interdisciplinary Science Beijing Institute of Technology Beijing P. R. China

Q

Qiang Zhang