What we investigate
Our group develops computational approaches that reveal how processing creates microstructure—and how microstructure governs performance.
DIGITAL TWINS + AI
We develop trustworthy, physics-informed digital twins that combine mechanistic simulations and deep learning to infer difficult-to-measure material parameters from microstructural morphology, with an initial focus on multi-principal-element alloys.
SEMICONDUCTORS + RELIABILITY
Through the UPWARDS collaboration, we are establishing a physics-informed digital-twin platform for semiconductor materials and reliability while supporting research and training across the U.S.–Japan microelectronics ecosystem.
MAGNETIC MATERIALS
We advance the fundamental understanding of nanostructure formation in multicomponent permanent-magnet alloys during magnetic-field-assisted manufacturing. Physics-based modeling and machine-learning microstructural quantification help us connect processing decisions to predictable magnetic properties.
EDUCATION + DATA SCIENCE
In collaboration with the University of Wisconsin–Madison, this program expands undergraduate research at the boundary of data science and materials science, grows a community of mentors and researchers, and builds a workforce fluent in materials informatics.
MATERIALS INFORMATICS
We use modern machine-learning methods, including deep learning, to quantify microstructures and construct the relationships that drive faster materials development and discovery.
DURABILITY + FRACTURE
Our models investigate how phase transformation, plastic deformation, microstructure, and crack growth interact to limit durability in shape-memory and other transformable materials.
MULTISCALE MODELING
We develop multiscale models that connect processing, structure, and properties in shape-memory ceramics—promising actuator materials for demanding thermal, pressure, and corrosive environments.