Intelligent Electrochemical Energy (IE²) Lab

IE^2

Research

3 min read

Research philosophy

My 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)
  • Enhanced Bayesian optimization techniques to handle data-scarce and noisy-label settings in real-world battery discovery
  • Created active learning frameworks that reduced experimental screening by 80% while maintaining discovery accuracy (Nat. Commun. 2025)
  • Developed solvent embedding techniques using graph neural networks to guide electrolyte discovery

Impact: Enabled rapid identification of promising electrolyte candidates, reducing development time from months to weeks.

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

Techniques: Ab initio molecular dynamics (AIMD), enhanced sampling methods, free energy calculations

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.

Software projects:

AtomBridge

  • Automated conversion of STEM images to crystal structures using LLMs and computer vision
  • Bridges the gap between microscopy and atomic-scale modeling
  • GitHub Repository
  • Award: 2025 Visionary Award at LLM Hackathon for Materials Science & Chemistry

curAItor-agent

  • Autonomous scientific data extraction from literature using LLMs and AI agents
  • Reduces manual data curation time by 95%
  • GitHub Repository

Refer Software page for more details

Mentoring philosophy

Future directions

  • Autonomous laboratories: Developing closed-loop systems that integrate ML, robotics, and simulations for self-driving materials discovery
  • Generative models: Creating novel molecular architectures beyond explored chemical spaces
  • Multi-scale modeling: Connecting atomistic insights to device-level performance
  • Sustainable chemistry: Designing earth-abundant, non-toxic materials for energy applications

Collaborative projects

Past experimental collaborations:

  • High-entropy alloys for electrocatalysis (with Prof. Krishanu Biswas, IIT Kanpur)
  • CO2 photoreduction catalysts (with Prof. S. C. Peter, JNCASR)
  • 2D materials synthesis and characterization (with Prof. Pulickel Ajayan, Rice University)
  • Fuel cell catalysts (with Prof. K. K. Nanda, JNCASR)

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