Our research focuses on fundamental understanding of the behaviour of materials in presence of defects, such as 0-D (vacancies, interstitials), 2-D (surfaces, interfaces), 3-D (nanoparticles). A major hurdle in developing next generation materials lies in understading the structure-property correlation at a fundamental level. More often than not, several types of defects coexist in materials used current and future technologies. We leverage computation to isolate all other effects to predict properties such as mechanical, cataltyic, and electronic.

Much of our predictions are centered on first-principles calculations based on density functional theory (DFT). We leverage graph neural networks trained from DFT data to predict properties in large systems. We also collaborate with experimental groups to design materials for current and next generation technologies. Current research interests include:

1. (1) Electrode and electrolyte materials for green hydrogen generation and conversion in fuel cell
2. (2) Catalytic process involved in production of Sustainable Aviation Fuel (SAF) production
3. (3) Structural and semiconducting materials under extreme environments.
4. (4) Charge transport through defects in solid state materials
5. (5) Surface and Interface chemistry through machine learned interatomic potentials based on DFT