About Me

I am a Graduate Ph.D. Student in the Department of Chemical Engineering at University of California Los Angeles working with Dr. Philippe Sautet. My research is in the field of computational material science and its application in surface science, electrochemical and energy storage systems, catalysis, and amorphous metallic systems. My research at UCLA utilizes Density Functional Theory with Machine Learning to understand metal/gas interfaces and surface reconstructions and identify new materials for chemical transformations. I received my M.S. in Chemical Engineering at Carnegie Mellon University where my thesis project was Dr. Venkat Viswanathan and I graduated with a B.Tech. in Chemical Engineering from the University of Petroleum and Energy Studies, Dehradun, India winning the silver medal (class topper). My CV is available here.

Research

My Ph.D. research, funded primarily by NSF, is a collaborative work with Dr. Sautet (UCLA) and Dr. Tao (previously at Kansas University). The aim of the work is to understand adsorbate-induced reconstruction. To be able to efficiently perform simulation on these complex systems involving surfaces with steps, kinks, and different coverages of adsorbates and reaction conditions, we utilize High Dimensional Neural Network Potentials (HDDNP) and fast exploration algorithms (Basin hopping, genetic algorithm etc.).

As a Computational Chemistry Intern at Phaseshift Technologies, I worked on generating a database for Bulk Metallic Glass (BMG) alloy properties to train machine learning models for mechanical property predictions. I also developed a framework for creating interatomic potentials for BMG with different compositions for rapid screening. The developed potentials were used successfully to predict and validate glass transition temperatures of different compositions.