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 |
|---|---|
| Article number | e202507387 |
| Journal | Angewandte Chemie - International Edition |
| Volume | 64 |
| Issue number | 30 |
| DOIs | |
| Publication status | Published - 21 Jul 2025 |
| Externally published | Yes |
Keywords
- Coulombic efficiency
- Electrolyte properties
- Lithium metal batteries
- Machine learning
- Pouch cell