• Materials modeling based on Density Functional Theory and Machine Learning to explore process-structure-property-performance relationships
• Bottom-up approaches bridged by crystallographic symmetry, statistical mechanics, and nucleation theory: from first-principles to thermodynamics/kinetics
• Development of computational methodology and its implementation in automation software
•  Leveraging the numerical methods of crystallography, machine learning, and graph theory into computational materials design

• Understanding the formation principle of atomic structures depending on process conditions
• Predicting the anisotropic interaction between solid surfaces and their environments
• Elucidating the peculiar unidirectional growth morphology of dendrite and nanowires

• Polar materials: III-V and II-VI for optoelectronics, (Hf,Zr)O2 for ferroelectrics, SrTiO3 for high-k and resistance-switching materials
• SnO for p-type oxide semiconductors
• Solid electrolyte interphase (SEI) and Li dendrite formed by Anode/electrolyte interface reactions in Li metal batteries