(2ar) Understanding and Controlling the Surface Physics and Chemistry of Complex Oxides

Fascinating and complex chemistry occurs at the surfaces and aqueous interfaces of metal-oxides. Understanding this chemistry could allow us to find answers to some fundamental questions concerning, e.g., next-gen electronic devices, sustainable hydrogen production, and even the origins of life on our planet. However, this diverse surface chemistry arises from an intricate medley of physical and chemical properties such as the electronic structure, redox state, and Brønsted acidity, making both experimental and theoretical studies challenging. In particular, this complicates the use of computational efforts to gain deeper insights, where a slew of methods is needed to capture emergent behavior across different time and length scales.

My independent research group will tackle and address challenges in describing and understanding collective phenomena at complex oxide surfaces and interfaces using a multi-scale computational approach. We will pursue three primary research directions in the initial part of my research program:

1. Understanding oxide-based (thermo/electro/photo)-catalyst dynamics and its influence on the chemistry
2. Deciphering the links between structural dynamics and oxide surface electronic structure
3. Unraveling the connections between mineral oxide-water interfaces and aqueous speciation of relevance to environmental geochemistry.

We will use my multidisciplinary expertise in quantum chemistry, statistical mechanics, solid-state physics, and deep learning to achieve these goals. This work promises to provide new insights into deciphering the surface physics and chemistry of oxides with immediate applications to heterogeneous catalysis, environmental sustainability, and materials technology.

Research Experience

My master’s, doctoral and postdoctoral training has prepared me adequately to lead this research program. Supported by a Rutgers School of Engineering Fellowship, I worked with Prof. Yee Chiew on understanding complex supercritical fluids during my master’s. I found critical links between the percolation threshold and the Widom line in supercritical oxygen, which sparked my continued interest to understand materials under extreme conditions. I began my foray into the world of oxide surface physics and chemistry as a doctoral trainee working with Prof. Aleksandra Vojvodic, supported by a graduate fellowship from the Vagelos Institute for Energy Science and Technology at the University of Pennsylvania. Over the course of my doctoral training, I established a framework for understanding the dynamics of surface dissolution of idealized oxide electrocatalysts and also developed a first-of-its-kind completely physics-based chemisorption model for doped semiconducting oxides. As a postdoctoral research associate with Prof. Annabella Selloni at Princeton University, I am harnessing the power of deep learning to represent the potential energy surface of reactive oxide-water interfaces and complex aqueous solutions, retaining the accuracy of ab-initio methods but at a fraction of the computational cost. Specifically, I have developed a deep potential model that accurately describes the acid-base equilibrium at the rutile IrO₂(110)-water interface and provides quantitative estimates of the point of zero charge (pH_PZC), hitherto unattainable with first-principles methods. This approach crucially provides links between atomistic factors and macroscopic observables, ushering in a new paradigm of understanding and predicting materials chemistry through improved synergy between computational models and experiments.

Teaching Interests and Experience

As a chemical engineer by training, I’m equipped to teach all courses at the undergraduate level, and the core graduate level courses. I’ve also had the experience of serving as a teaching assistant for both undergraduate and graduate-level thermodynamics and statistical mechanics over the course of my master’s and doctoral training. Further, I’m interested in developing a graduate-level course that aligns with my research interests in multiscale computational methods. Specifically, I envision a course that covers the fundamentals of everything from electronic structure methods to deep learning-based data science approaches, along with providing their practical application to solving real-world problems involving chemistry in the condensed phase.