We develop atomistic modeling methods and computer codes to study electronic and energy materials and processes.
Currently we focus on electronic transport, electrocatalysis, 2D materials, semiconductors, and AI for science.
On the chemistry side, we study the electrocatalysis for energy conversion and storage. Example reactions include water splitting, CO2 conversion, oxygen reduction, nitrogen/nitrate reduction. We develop more realistic models to better describe the complex electrochemical interface, and use them to better understand and predict electrocatalysts. Particularly, we develop machine learning (ML) force fields to enable large scale simulations
(Picture for our review paper, selected as journal cover by Chemical Reviews）
On the physics side, we study the electronic transport for electronic and optoelectronic applications. We develop first-principles methods to accurately and efficiently calculate the scatterings of electrons by phonons, point defects, surfaces, and grain boundaries; and use them to compute, understand and predict the transport properties such as conductivity, mobility, and saturation velocity.
We also have experience and interest in other topics, such as crystal growth and thermo-catalysis, and are open to collaborations and new opportunities.