In our group, we develop theoretical approaches to predict the performance of electrocatalysts. We use machine learning, optimization methods, kinetic Monte Carlo models, and density functional theory (DFT) to simulate, predict, and optimize the performance of electrocatalysts.
We combine these tools in high-throughput screenings to propose new electrocatalysts with optimized performance. Together with our experimental collaborators, we work on implementing the proposed catalysts in experimental synthesis. Results from experimental measurements will then help improve the theoretical models.
Covered Topics: Electrochemistry, DFT, kinetic Monte Carlo, software development.
