Battery science from atomic interfaces to commercial cells.
Energy Storage & Conversion Lab · University of Arizona
We combine electrochemical experiments, multiscale simulation, high-performance computing, and artificial intelligence to understand degradation, prevent thermal failure, and design next-generation energy-storage materials.
How do atomic structure, electrochemical interfaces, and operating conditions interact to govern battery performance, degradation, and safety?
We answer this question across scales—from atomic configurations and evolving interfaces to commercial cylindrical and pouch cells—by integrating experiments, physics-based simulation, high-performance computing, and artificial intelligence.
Battery Cells, Diagnostics & Safety
We test commercial-format batteries under realistic electrical and environmental conditions to identify the signals that precede degradation and thermal failure.
- Commercial-format cells and dynamic cycling
- Impedance, thermography, and environmental control
- Degradation diagnostics and thermal-event forecasting
Materials, Interfaces & Intelligent Design
We connect atomic disorder and electrochemical interfaces to measurable material and cell behavior using first-principles calculations, multiscale simulation, high-performance computing, microscopy analysis, and generative artificial intelligence.
- Electrochemical interfaces and evolving microstructures
- High-entropy materials and structure–property relationships
- AI-assisted microscopy and candidate generation
Shared methods, one research loop
Measure →
Electrochemical, thermal, impedance, environmental, and dynamic testing.
Model →
Atomistic, phase-field, electrochemical–thermal, and continuum simulation.
Learn →
Machine learning, microscopy analysis, surrogate models, and generative methods.
Translate
Safety forecasting, sensing, commercial-cell validation, and technology development.
Experiments and computation in one connected research environment
Battery cycling, electrochemical impedance, infrared thermography, environmental testing, vibration, and mechanical measurements are planned together with multiphysics modeling, high-performance computing, GPU acceleration, and machine learning. The result is a connected workflow for resolving mechanisms, validating predictions, and improving battery performance and safety.
