Machine learning for evaluation of electrode structure and battery characteristics

Machine learning for evaluation of electrode structure and battery characteristics

Prediction of performance of electrodes using machine learning is conducted, especially, focusing on porous electrodes structure.
For machine learning, we use cross-sectional images of electrodes, 3D structures, charge/discharge curves, etc., to build predictive models.

Other Protocols

• Electrode particle design based on reaction transport models
• 3D Structure Analysis
• Operando Electrode Reaction Analysis
• Operando Nano X-ray CT Imaging
• Operando Soft X-ray Absorption Spectroscopy
• Operando Mechanical Characterization of Battery Cross‑Sections
• Explosion-Proof Operando X-ray CT Imaging
• Rapid safety screening with small batteries
• Autonomous Experimentation System for Electrolyte Materials Discovery
• High-Throughput Pouch Cell Production and Evaluation Platform
• Cathode & Solid Electrolyte Database
• Data-Driven Inorganic Materials Design Tool