Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries
Problem
Discovering ionic liquids for ionic polymer electrolytes to enable safe, high-energy-density lithium metal batteries.
Why it matters
Safer, high-energy-density batteries are needed, and flammable organic components are a safety concern.
What the AI did
A machine learning workflow embedding quantum calculation and a graph convolutional neural network screened and recommended candidate ionic liquids, which were then used to build polymer electrolyte membranes.
Results
~50 μm, >200 MPa membranes; 6 mA cm−2 critical current density at 80 °C; >96% capacity retention at 0.5C and >80% at 2C over 350 cycles; 146 mAh g−1 at 3C; >99.92% efficiency.
Impact
Peer-reviewed in Nature Communications with concrete measured cell performance, supporting ML-guided materials discovery.
Record
| Venue | Nature Communications |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Kai Li, Jifeng Wang, Yuanyuan Song, Ying Wang |
| Citations | 115 (OpenAlex, at import time) |
| References | 65 |
| Model or method | Graph convolutional neural network (with quantum calculation) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1038/s41467-023-38493-7 |
| Open access | gold |
| Keywords | machine learning, ionic liquids, polymer electrolytes, lithium metal batteries, graph neural network |
Importance rating
significant — thousands to millions affected
Enables safe, high-energy-density lithium metal batteries, a major energy-storage goal; abstract reports strong measured cell performance but no scale of impact.