Research
Computational Electrocatalysis
Computational catalysis leverages quantum chemical simulations—primarily density functional theory (DFT)—to understand, predict, and design catalytic materials and their reactivity at the atomic level. This field enables the systematic exploration of reaction mechanisms, identification of active sites, and rational screening of promising catalysts, all while capturing the influence of electronic structure and local environment on performance. By simulating surface reconstruction, mapping multistep reaction networks, and tuning the interfacial environment under operating conditions, we aim to accelerate the discovery of efficient catalysts that support industrial decarbonization and sustainable energy conversion.
Metal Alloys
Nat. Catal. 6, 939–948 (2023)Nat. Catal. 5, 564–570 (2022)Nat. Energy 7, 170–176 (2022)
Artificial Intelligence in Electrocatalysis
Artificial intelligence (AI) is increasingly transforming the field of catalysis by offering powerful tools to address the vast complexity of catalytic materials and reaction networks. The chemical space of possible catalysts—ranging from metals and oxides to alloys and nanostructures—is enormous, and AI enables systematic exploration by learning from high-dimensional datasets and guiding simulations toward promising regions. In computational catalysis, AI methods such as supervised learning, active learning, and delta-learning can significantly reduce the cost of quantum chemical calculations and identify hidden patterns across different catalytic systems. These approaches help bridge the gap between idealized models and real-world systems, accelerating the discovery of catalysts that support clean energy conversion and sustainable chemical manufacturing.
