Designing battery materials by understanding interfaces and atomic disorder
Materials, Interfaces & Intelligent Design
First-principles calculations, multiscale simulation, high-performance computing, microscopy, and generative AI reveal how interfaces and atomic structure govern transport, stability, and performance.
A cross-scale design problem
Atomic arrangements and electrochemical interfaces evolve into measurable changes in transport, stability, degradation, and cell behavior.
We connect those scales by combining mechanistic simulation with experimental evidence and intelligent data-driven methods—so that prediction remains anchored to physics and design decisions remain testable.
Electrochemical interfaces and kinetics
Interfacial reactions translate atomic-scale energetics into measurable current, voltage, transport limitations, and degradation.
- Interfacial charge transfer
- Coupled transport and reaction
- Advanced kinetic descriptions beyond idealized rate laws
- Connection to measurable electrode and cell behavior
First-principles, electrochemical, and continuum models are combined to identify the mechanisms that control performance and stability.
Solid-electrolyte interphase
The SEI is not a uniform film: its grains, grain boundaries, chemistry, and evolving microstructure create distinct pathways for ion transport and degradation.
- Polycrystalline SEI structure
- Grain and grain-boundary transport
- Ionic conductivity and stability
- Microstructure evolution during cycling
Atomistic energetics and mesoscale transport models are connected to determine how local structure controls effective interphase behavior.
Electrodeposition and morphology
Metal deposition couples electrochemistry, transport, mechanics, and substrate structure, producing morphologies that can determine lifetime and safety.
- Lithium and zinc electrodeposition
- Dendrite and filament nucleation and growth
- Phase-field modeling of evolving morphology
- Mechanical stress and substrate effects
Simulations resolve how operating conditions and interfacial properties select compact, porous, or unstable growth modes—and identify testable routes to control them.
High-entropy materials and atomic disorder
High-entropy oxides and alloys contain immense configurational spaces, but only a small fraction of atomic arrangements produce useful electrochemical behavior.
- Atomic occupancy and short-range order
- Defects, local environments, and structure–property relationships
- Grain-boundary evolution and phase stability
- Density functional theory and high-performance screening
Physics-based calculations resolve which motifs are stable and informative, creating validated training data for intelligent candidate generation.
AI for microscopy and materials generation
Artificial intelligence connects what we observe in microscopy with the configurations we want to discover—while physics and uncertainty checks keep every prediction testable.
- Element mapping and atomic-column analysis
- Microstructure segmentation and classification
- Transformer-based generation of atomic occupancy patterns
- Uncertainty quantification and physics-based validation
Learned representations link images, structures, and properties so candidate materials can be prioritized for first-principles calculation and experimental follow-up.
Methods that connect structure to behavior
First-principles
Density functional theory, atomic energetics, defects, occupancy, and electronic structure.
Multiscale simulation
Phase-field, electrochemical, and continuum models for evolving interfaces and microstructures.
High-performance computing
Parallel workflows, GPU acceleration, parameter studies, and large candidate spaces.
AI & microscopy
Image analysis, surrogate models, representation learning, and generative structure design.
NSF-funded project · Materials, interfaces & intelligent design
CDS&E: Generative Modeling of Atomic Disorder in High-Entropy Oxide Materials
NSF 2602117 · $578,383 · August 1, 2026–July 31, 2029
Density functional theory, high-performance computing, and Transformer-based generative models will learn atomic occupancy patterns and short-range order in high-entropy oxide battery cathodes.
Principal Investigator: Vitaliy Yurkiv · Status: Awarded; project begins August 1, 2026
This project advances one focused direction within the lab’s broader effort to connect atomic structure, evolving interfaces, and measurable electrochemical behavior.
Related publications
The publications record connects model development with electrochemical interfaces, computational materials, high-entropy systems, microscopy, and AI-assisted discovery.
Browse peer-reviewed work on interfacial kinetics, dendrite and microstructure evolution, first-principles materials calculations, microscopy analysis, and generative modeling.