Deciphering Coulombic Efficiency of Lithium Metal Anodes by Screening Electrolyte Properties

Zhao Zheng, Xinyan Liu, Xue Qiang Zhang*, Shu Yu Sun, Jia Lin Li, Ya Nan Wang, Nan Yao, Dong Hao Zhan, Wen Jun Feng, Hong Jie Peng, Jiang Kui Hu, Jia Qi Huang, Qiang Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalAngewandte Chemie - International Edition
DOIs
Publication statusAccepted/In press - 2025

Keywords

  • Coulombic efficiency
  • Electrolyte properties
  • Lithium metal batteries
  • Machine learning
  • Pouch cell

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