Research philosophy
Our research combines artificial intelligence, computational chemistry, and experimental validation to accelerate the discovery of materials for sustainable energy applications. I focus on developing data-driven frameworks that leverage machine learning, molecular simulations, and high-throughput experimentation to design next-generation battery electrolytes and catalysts.
Research themes
1. AI-driven electrolyte discovery for next-generation batteries
Objective: Accelerate the discovery of optimal electrolyte formulations for lithium metal and anode-free batteries using machine learning.
Key contributions:
- Developed Electrolytomics: A unified big data approach integrating multiple electrolyte databases (20,000+ entries) for systematic design and discovery (Chem. Mater. 2025)
- Created active learning frameworks that reduced experimental screening by 80% while maintaining discovery accuracy (Nat. Commun. 2025)
2. Understanding electrolyte effects in electrochemical reactions
Objective: Elucidate fundamental mechanisms of how aprotic electrolytes influence electrochemical transformations.
Key discoveries:
- Discovered that water clustering in aprotic media modulates activity and enables hydrogenated product formation during CO electroreduction (JACS 2025)
- Revealed how water hydrogen bonding within nonaqueous environments controls reactivity in electrochemical transformations (Nat. Catal. 2024)
- Formulated computational descriptors from MD simulations to predict electrolyte performance in CO₂/CO reduction
3. Machine learning for materials property prediction
Objective: Develop interpretable ML models for accurate prediction of materials properties.
Achievements:
- Introduced chemical hardness-driven interpretable ML for rapid photocatalyst screening, achieving 90% accuracy with 10× speed improvement (NPJ Comput. Mater. 2021)
- Created graph neural networks (GNNs) for predicting electrolyte properties including conductivity, viscosity, and electrochemical stability
- Developed transfer learning approaches to overcome data scarcity in materials discovery
4. Rational design of catalysts
Objective: Establish design principles for efficient catalysts in key industrial reactions.
Major contributions:
- Developed electronic structure-based design principles for single-atom catalysts in nitrogen reduction reaction (ChemCatChem 2020)
- Established universal criteria for Dirac cone splitting in 2D materials for electronic applications (J. Phys. Chem. C 2019)
- Designed van der Waals heterostructures (C2N/WS2) as promising water splitting photocatalysts (J. Catal. 2018)
5. AI-Powered scientific software development
Objective: Build tools that automate scientific workflows and accelerate discovery.
Note: Refer Software page for implementation of some of our research projects.