Research philosophy
Our research combines artificial intelligence (AI), computational physics/chemistry/materials science, and scientific theory to accelerate the discovery of materials for sustainable energy applications. We focus on developing data-driven frameworks that leverage machine learning, molecular simulations, and high-throughput experimentation to design next-generation battery electrolytes and catalysts.
Mentoring philosophy
Research themes (overarching)
1. Battery data extraction through automated literature mining
2. Universal machine learning force field development for battery electrolytes
3. AI-driven materials discovery for next-generation batteries
4. Multiscale modeling for understanding interfacial and interphasial phenomena
Note: Refer Software page for implementation of some of our research projects.