스테판링에 연구실

We work across three connected scales, from the electronic structure of a catalyst surface up to the behaviour of a full electrolyser. Machine-learning-driven quantum chemically accurate calculations of the catalyst itself: which sites are active, how adsorbates bind, and how the surface reorganises under reaction conditions. The questions that set everything downstream are decided here — activity and selectivity descriptors, the reaction mechanism, and whether a material stays intact at operating potential. The region between catalyst and electrolyte, where the electric double layer, the local pH and the ion distribution together decide the rate. We model the interface by combining high-level quantum chemistry with multi-scale modeling techniques (many body expansion and machine learning potentials), deriving a predictive, atomistic picture of the interface and reaction processes. Coupling the interface description to transport across a whole device: reactions, diffusion, migration and convection through a porous gas diffusion electrode. This is where a catalyst that looks good on paper meets the cell it has to work in, and where cell design turns out to govern which products come out. Open packages we build and maintain, from the electronic structure of a surface through to a full electrolyte cell. Reads a CatMAP microkinetic specification and produces the rate constants a simulation needs, via transition-state theory. It is one layer of a Julia stack: Catalyst.jl expresses the reaction networks, and LiquidElectrolytes.jl solves the coupled electrolyte transport. Modern electrode designs — porous materials, gas diffusion layers, solid electrolyte membranes — need kinetics and mass transport treated on an equal footing. CatINT provides the interface between the two, co