The catalyst plays a crucial role in activating the desired reactions by interacting with the reactant, intermediate, and product species. An active and stable catalyst is crucial in improving the efficacy of electrochemical conversions. Depending upon the operating temperatures, the bulk and surface of the catalyst materials can be involved differently in the catalytic processes. However, the catalyst discovery relies largely on trial-and-error approach, which is slow and resource-intensive.

At the Li Energy Lab, we integrate data-driven models, machine learning tools, and mechanistic insight to guide catalyst design more intelligently. By linking material features with catalytic behaviour, we aim to accelerate discovery and reduce reliance on traditional, labour-intensive screening.