(4kk) Engineering Catalysts through Machine Learning, Experimental, and Density Functional Theory Methods for Sustainable Energy Applications

Leveraging and advancing machine learning, experimental and density functional theory methods to 1) investigate the oxygen binding properties and collective catalytic behaviors of metal ions in oxides and nitrides; 2) engineer for applications in the hydrogen industry, including electrolyzers and fuel cells, and extending to energy storage technologies such as supercapacitors and batteries.

Teaching Interests:

Holding both Bachelor's and Ph.D. degrees in Chemical Engineering, I feel confident and capable of teaching core chemical engineering courses at both undergraduate and graduate levels. Additionally, I am interested in teaching courses that align with and reflect my interdisciplinary research interests, including soft matter and interfacial phenomena, physical chemistry, machine learning for chemistry as well as data-driven catalyst design and polymer science.

Climate change poses a global existential challenge, necessitating a significant shift from fossil fuels to sustainable energy sources such as green hydrogen, derived from renewable resources, to decarbonize transportation and various industries. This talk will illustrate how a chemical engineering approach advances catalyst design for fuel cells and electrolyzers ultimately based on green hydrogen. My presentation will cover two critical aspects: compositional design and manufacturing processes. Initially, I will focus on integrating machine learning with density functional theory to model the catalytic performance of transition metal ions in oxides, including binding properties, resulting from complex interactions. This effort includes validating our models through experimental measurements. Subsequently, I will employ machine learning and optimization techniques to enhance the generation of metal oxides via colloidal synthesis.