Volume 64, Issue 30 e202507387
Research Article

Deciphering Coulombic Efficiency of Lithium Metal Anodes by Screening Electrolyte Properties

Dr. Zhao Zheng

Dr. Zhao Zheng

Beijing Key Laboratory of Complex Solid State Batteries & Tsinghua Center for Green Chemical Engineering Electrification, Department of Chemical Engineering, Tsinghua University, Beijing, 100084 P.R. China

Both authors contributed equally to this work.

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Dr. Xinyan Liu

Dr. Xinyan Liu

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

Both authors contributed equally to this work.

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Dr. Xue-Qiang Zhang

Corresponding Author

Dr. Xue-Qiang Zhang

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

E-mail: [email protected]; [email protected]

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Shu-Yu Sun

Shu-Yu Sun

Beijing Key Laboratory of Complex Solid State Batteries & Tsinghua Center for Green Chemical Engineering Electrification, Department of Chemical Engineering, Tsinghua University, Beijing, 100084 P.R. China

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Jia-Lin Li

Jia-Lin Li

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

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Ya-Nan Wang

Ya-Nan Wang

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

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Nan Yao

Nan Yao

Beijing Key Laboratory of Complex Solid State Batteries & Tsinghua Center for Green Chemical Engineering Electrification, Department of Chemical Engineering, Tsinghua University, Beijing, 100084 P.R. China

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Dong-Hao Zhan

Dong-Hao Zhan

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

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Wen-Jun Feng

Wen-Jun Feng

Beijing Key Laboratory of Complex Solid State Batteries & Tsinghua Center for Green Chemical Engineering Electrification, Department of Chemical Engineering, Tsinghua University, Beijing, 100084 P.R. China

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Prof. Hong-Jie Peng

Prof. Hong-Jie Peng

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

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Jiang-Kui Hu

Jiang-Kui Hu

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

The Innovation Center for Smart Solid State Batteries, Yibin, 644002 P.R. China

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Prof. Jia-Qi Huang

Prof. Jia-Qi Huang

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

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Prof. Qiang Zhang

Corresponding Author

Prof. Qiang Zhang

Beijing Key Laboratory of Complex Solid State Batteries & Tsinghua Center for Green Chemical Engineering Electrification, Department of Chemical Engineering, Tsinghua University, Beijing, 100084 P.R. China

Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084 P.R. China

E-mail: [email protected]; [email protected]

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First published: 21 May 2025

Graphical Abstract

Both 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. A regression model is further proposed to estimate the CE based on β and HOMO-LUMO gap, which provides a reliable interpretable quantitative model for rational electrolyte design.

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.

Conflict of Interests

The authors declare no conflict of interest.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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