Deciphering Coulombic Efficiency of Lithium Metal Anodes by Screening Electrolyte Properties.
Zheng Zhao, Xinyan Liu, Xue‐Qiang Zhang, Shu‐Yu Sun +9
AI summary
75% confidenceThis MES study investigates machine learning, coulombic efficiency, anode. Key performance metrics include coulombic efficiency.
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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. Hydrogenbond 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 HOMOLUMO gap. Using the new solvent screened by above regression model, the Li 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.
Keywords
Identifiers
- PubMed
- 40396912
- Journal
- Angewandte Chemie
- Year
- 2025