研究领域

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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.