Our Research
The Theoretical Catalysis Group has been at the forefront of exploring surface reactions in electrocatalysis, specifically targeting the carbon dioxide electrochemical reduction and the oxygen evolution reaction. The group’s research endeavors are categorized into three main areas:
1. Advancing computational chemistry methodologies to accurately represent the realistic liquid-solid interface, encompassing solvent and electric field effects.
2. Leveraging cutting-edge machine learning techniques to enhance the efficiency of computational chemistry processes.
3. Delving into the reaction kinetics and thermodynamics of electrocatalysis systems through the use of density functional theory and microkinetic modeling.
This focused approach not only aims to advance the understanding of elementary steps and reaction mechanisms but also to pioneer new avenues in the field of electrocatalysis.
Machine Learning
We use machine learning methods to automatically learn and improve from computational chemistry data by utilizing algorithms and statistical models.
Computational chemistry
We explore catalytic mechanisms and kinetics at the atomic level through micro-kinetic modeling and density functional theory calculations.
Electrocatalysis
We understand how electrical energy can drive chemical reactions, typically through applying electricity to facilitate fuel cells and electrolysis.