Research philosophy: create understand disseminate
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.
We also strive to deliver tangible impact through development of open-source software and utilities to enable the broader community to benefit from our research. Apart from directly benefitting broader research community, we are also interested in developing accessible materials targeted towards budding minds to pique their interest in STEM and sustainable energy research.
Mentoring philosophy: foster growth mindset
Research themes
1. Battery data extraction through automated literature mining
2. Universal machine learning force field development for battery electrolytes
3. AI-accelerated materials discovery for next-generation batteries
4. Multiscale modeling for unraveling interfacial and interphasial phenomena
5. Physics-informed battery lifetime prediction
Note: Refer Software page for implementation of some of our research projects.