Two research thrusts. One cross-scale battery challenge.
Energy Storage & Conversion Lab · University of Arizona
We connect atomic structure, electrochemical interfaces, and operating conditions to battery performance, degradation, and safety.
Understanding mechanisms is the path to prediction and design.
How do atomic structure, electrochemical interfaces, and operating conditions interact to govern battery performance, degradation, and safety?
Experiments reveal observable behavior. Physics-based models resolve hidden mechanisms. Artificial intelligence identifies patterns and generates candidate configurations. Validation connects atomic-scale knowledge to commercial-format cells.
Battery Cells, Diagnostics & Safety
We study commercial cylindrical and pouch cells under realistic electrochemical and environmental conditions.
Cycling, impedance spectroscopy, infrared thermography, controlled thermal exposure, and data-driven diagnostics reveal how degradation develops—and which measurable signals can provide earlier warning of unsafe thermal events.
Across scales: cell signals → evolving internal state → degradation and thermal risk → sensing and mitigation.
Materials, Interfaces & Intelligent Design
We investigate solid-electrolyte interphases, interfacial kinetics, lithium and zinc electrodeposition, dendrite formation, high-entropy oxide cathodes, and atomic disorder.
First-principles and phase-field modeling, high-performance computing, microscopy-informed analysis, and generative AI help us explain mechanisms and design promising material configurations.
Across scales: atomic structure → interface evolution → transport and stability → electrode and cell behavior.
Shared methods across scales
Experiment
Electrochemical cycling, impedance, thermography, environmental control, vibration, and mechanical testing.
Model
First-principles, phase-field, electrochemical–thermal, and continuum simulation.
Compute
High-performance computing, GPU acceleration, parameter studies, and scalable workflows.
Learn
Machine learning, microscopy analysis, surrogate models, and generative artificial intelligence.
Funded projects
Current federal projects are integrated into the research thrusts they support, connecting fundamental mechanisms to measurable cell behavior and safer energy-storage systems.
Battery diagnostics & safety
Machine Learning-Assisted Forecasting of Thermal Events in Rechargeable Batteries
U.S. Department of War (DoD DEPSCoR)
FA9550-24-1-0164 · $599,808 · June 2024–May 2027
PI: Vitaliy Yurkiv · Co-PI: Todd A. Kingston, Iowa State University
Experimental, multiphysics, and machine-learning methods for sensing, forecasting, and mitigating thermal events in rechargeable batteries.
Read the University of Arizona project story →
Historical award metadata issued in 2024 used “Department of Defense”; that original wording and award identifier are preserved in the source record.
Materials, interfaces & intelligent design
CDS&E: Generative Modeling of Atomic Disorder in High-Entropy Oxide Materials
U.S. National Science Foundation · Division of Materials Research
NSF 2602117 · $578,383 · August 1, 2026–July 31, 2029
PI: Vitaliy Yurkiv · Status: Awarded; project begins August 1, 2026
Density functional theory, high-performance computing, and Transformer-based generative models for learning atomic occupancy patterns and short-range order in high-entropy oxide battery cathodes.