(4bf) Computational Catalysis and Machine Learning Guided Catalyst Design and Discovery for Energy Applications

Catalysts are the workhorses of chemical transformations in the production of clean transportation fuels and chemicals, and renewable energy technologies. Designing efficient catalysts that satisfy activity, selectivity, stability, and cost requirements are critical to achieving a sustainable energy future. Catalyst design often start from molecular-scale hypotheses about the reaction mechanism, the structure of the active sites, and the nature of the steps determining rate and selectivity. Recently, computational catalysis has become a crucial tool to analyze molecular-scale hypotheses and elucidate their electronic origin, to understand experimental trends, and to screen hundreds of catalysts in silico using descriptor-based approaches to design and discover promising new catalytic materials.

During my postdoctoral research over the past year with Prof. Jens K. Nørskov at the Catalysis Theory Center, Technical University of Denmark, I discovered acid-stable and active materials for oxygen electrocatalysis by developing a computational framework to screen metal oxides using high-throughput density functional theory (DFT) calculations and machine learning (ML). Promising new catalytic materials were then experimentally tested and validated for catalytic performance in collaboration with Prof. Ib Chorkendorff’s group at the Technical University of Denmark. I further developed a theoretical framework to gain insights on the stability and activity of single-atom catalysts (SACs) supported on oxides for oxygen electrocatalysis. Moreover, I collaborated with Prof. Thomas Jaramillo’s group at the Stanford University to design Ag-based bimetallic catalysts for oxygen reduction reaction (ORR) in alkaline media.

In my Ph.D. research, with Prof. Mark Saeys and Prof. Guy B. Marin at the Laboratory for Chemical Technology, Ghent University, I introduced new mechanistic pathways for CO activation and chain growth for cobalt-catalyzed Fischer-Tropsch synthesis (FTS). I then developed a comprehensive dual-site microkinetic model for FTS, accounting for catalyst structure, coverage, and reaction conditions and investigated the product selectivity, catalytic activity, and reaction pathways. I examined CO adsorption on cobalt and platinum using ab intio thermodynamics and bonding concepts, and established design principles for tuning catalytic activity using surface charge as a descriptor.

Future plans for my independent research group will focus on developing new catalysts and catalyst design principles for energy applications. I will focus on identifying reaction mechanisms, nature of active sites, and steps determining activity and selectivity using DFT and microkinetic modeling. These insights will enable to identify new descriptors controlling the catalyst activity and selectivity. High-throughput computational screening studies (using a DFT and ML framework) are then performed to discover next-generation materials for energy applications.

Teaching Interests:

My teaching philosophy is to act as a catalyst to inspire the next generation of students. I believe that a successful teacher clearly communicates complex concepts to diverse audiences, sparks curiosity, enables critical thinking, engages enthusiastically with students, and stimulates the quest for advancing the state-of-the-art in science and technology. My prior teaching experiences include preparing and delivering lectures and conducting laboratory sessions in undergraduate and graduate courses. To date, I have had the pleasure of supervising eleven undergraduate honors students, two graduate students in research projects involving computational catalysis. With a strong background in core Chemical Engineering subjects and teaching experience, I would be interested in teaching any fundamental chemical engineering course. Given my scientific background in computational catalysis, I would especially be interested to develop 2 courses titled ‘Fundamentals of Heterogeneous Catalysis with Applications in Energy Transformations’ and ‘Data Science for Chemical Engineers’.

I am committed to promoting diversity and inclusion in my independent research group and research community. I strongly endorse STEM outreach programs which aim to encourage non-traditional students in to STEM fields and I have been involved in several such activities. I also enjoy science communication with non-specialists and during my Ph.D., I gave a TED­x talk titled ‘Hacking the carbon cycle using computers’ to share my research with the general public.