Applications
- Dielectric film capacitors
- Electrostatic energy storage materials
- Electronics components and polymer material manufacturers
Advantages/Benefits
- Rapid polymer discovery via machine learning significantly shortens development cycles
- Achieves optimal balance of thermal resistance and electrical insulation
- Demonstrates high energy density and cycling efficiency at 200 ℃
Background
The development of heat-resistant dielectric polymers that withstand intense electrical fields at high temperatures is critical for electrification. Traditional intuition-driven polymer design approaches result in a slow discovery loop that limits breakthroughs. There is a need for a way to identify polymers for dielectric capacitors quickly and accurately.
Technology Overview
Scientists at Berkeley Lab have developed a machine-learning driven strategy to rapidly identify high-performance, heat-resistant polymers. Thousands of different polysulfate polymers were identified and screened by machine learning for capacitor properties. The use of sulfur fluoride exchange click chemistry reaction enables successful synthesis and validation of selected candidates, particularly a high-performance polysulfate featuring a 9,9-di(naphthalene)-fluorene repeat unit, which exhibits excellent thermal resilience and achieves ultrahigh discharge energy density with high efficiency at 200 ℃. Its exceptional cycling stability underscores its promise for applications in demanding electrified environments.
Development Stage
TRL 5 – Laboratory scale, similar system validation in relevant environments.
For More Information
Li, H., Zheng, H., Yue, T. et al. Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage. Nat Energy 10, 90–100 (2025). https://doi.org/10.1038/s41560-024-01670-z
Principal Investigator(s)
- Zongliang Xie
- He Li
- Yi Liu
Status
Patent pending
Opportunities
Available for licensing or collaborative research
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