Machine Learning and Optimization
Assistant Professor, Materials Science and Engineering
The Mesoscale Computational Modeling group computationally models the evolution of material microstructure as well as properties mainly by phase-field modeling. This can provide fundamental understanding for an experimental observation or measurement, and guidance to experiments to achieve or optimize a desirable property or functionality. It is also an imperative component in data-driven computational materials design.
They currently focus on materials that exhibit coupling effects, including magnetostrictive, piezoelectric, ferroelectric, magnetoelectric, and caloric materials. They are also interested in the transport properties of materials, centering on ion transport in solid electrolytes. They develop continuum theories and phase-field models to understand how material microstructure affects these coupling effects and transport properties. They also use phase-field model to design materials microstructure for optimum effects or properties.