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 language | English |
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Journal | Angewandte Chemie - International Edition |
DOIs | |
Publication status | Accepted/In press - 2025 |
Keywords
- Coulombic efficiency
- Electrolyte properties
- Lithium metal batteries
- Machine learning
- Pouch cell