top of page
Oxidized graphene


Computational Catalysis for Clean Energy Transformation

Transitioning to sustainable sources of energy is essential to reducing rising levels of CO2 in the atmosphere. Renewable energy technologies such as fuel cells and metal-air batteries are set to play an important role while electrolysis cells can tackle the important clean energy challenge in transforming low-value chemicals to valuable compounds. Catalysts (critical materials that boost the rate of chemical reactions) are at the heart of all clean energy technologies; the current lack of efficient catalysts severely prohibits these technologies from becoming more economically viable.

Descriptor-Based Analysis


Our group uses quantum mechanical, in particular density functional theory (DFT), calculations to understand the frontiers of existing catalysts with the goal of designing  more efficient catalysts materials for clean energy conversion and environmental protection. We correlate the electronic structure properties of the catalysts to their activity, selectivity and stability using descriptor-based analysis, a set of critical steps that include: a) identifying thermodynamically favorable reaction pathways using full mechanistic study, b) identifying the critical reaction steps that define descriptors of the reaction, and c) calculating transition state energies to understand the rate of the reactions. This approach has proven to be extremely powerful in allowing large screening of catalyst materials space and providing the lead to identifying the most efficient catalysts for a variety of reactions.  It is also a uniquely valuable approach to avoid the time-consuming and expensive process of catalyst design through experimental pipelines.

Electrochemical Devices for Transitioning to Zero/Net Carbon Future (Our Current Research Areas)

Screen Shot 2023-07-29 at 8.58.31 AM.png

Establishing Structure Activity Relationship

Screen Shot 2020-11-06 at 3.08.46 PM.png
bottom of page