(6cr) Computational Design and Development of Advanced Catalytic Materials

The central goal of my research is to computational discover, design and develop advanced functional materials for energy applications, e.g. electrocatalysis. The multiscale computational tools e.g. density functional theory, ab initio molecular dynamics and many body perturbation theory (GW/BSE) and high-throughput computational method e.g. machine learning will be integrated together to achieve the goal. Specific interested objectives are unconventional atomic structures that have great potentials in future electrochemical catalysis. I aim to develop the hybridized nano-structures combing the advantages of homogenous and heterogeneous catalysts with expected functions based on computational modeling. Engineering approaches from both intrinsic and extrinsic factors such as defect, doping, alloying and applying mechanical deformations will be applied to tune the materials towards desired properties. The high-throughput and machine learning models will be integrated to accelerate the advanced catalysts design process.

Teaching Interests:

My philosophy of teaching is to create a heuristic teaching environment. I can teach materials related courses, including thermodynamics, mechanics of materials, electronic structure of materials, solid state physics as undergraduate level courses. I also can provide courses related to advanced computational method such as density functional theory based simulations to teach the students helpful skills for their research. Depending on my research progress, I am highly interested in opening an interdisciplinary course on machine learning in materials science, which is a thrusting and cutting-edge topic in the materials science area.