Publications of Karsten Reuter

C. Staacke: The Electrostatic Gap: Combining Electrostatic Models with Machine Learning Potentials. Technische Universität München

S. Stegmaier: Working Interfaces in Solid-State Electrolytes: A Theoretical Approach towards Realistic Models. Technische Universität München

S. Stocker: Transferability in chemical machine learning. Technische Universität München

S. Wengert: Kernel-based machine learning for molecular crystal structure prediction. Technische Universität München

C. Grosu: Capturing ion dynamics in lithium intercalated graphite: bridging the gap between experiment and theory through advanced nuclear magnetic resonance and multiscale modeling. Technische Universität München

S. Döpking: Error aware analysis of multi-scale reactivity models for chemical surface reactions. Freie Universität Berlin

C. Kunkel: Data-driven Organic Semiconductor Discovery. Technische Universität München

H. Guhl: Density functional theory study of oxygen and water adsorption on SrTiO3(001). Humboldt-Universität Berlin

S. Matera: A First-Principles Based Multiscale Approach from the Electronic to the Continuum Regime: CO Oxidation at RuO2(110). Technische Universität Berlin Berlin

M. Rieger: First-principles based models for lateral interactions of adsorbates. Freie Universität Berlin

A.G. Sanfilippo: An ab-initio study of bilayer graphene using higher order quantum chemical methods. Freie Universität Berlin

E.R. McNellis: First-principles modeling of molecular switches at surfaces. Freie Universität Berlin

R. Gehrke: First-principles basin-hopping for the structure determination of atomic clusters. Freie Universität Berlin

Y. Zhang: First-principles statistical mechanics approach to step decoration at solid surfaces. Freie Universität Berlin

J. Rogal: Stability, composition and function of palladium surfaces in oxidizing environments: A first-principles statistical mechanics approach. Freie Universität Berlin

K. Reuter: First-principles statistical mechanics for oxidation catalysis. Freie Universität Berlin Berlin

T. Huss: Towards a universal machine learning interatomic potential for the xLi₂S-(100 - x)P₂S₅ material class. Technische Universität München

P. König: Generative Adversarial Networks (GANs) for inverse design of RuO₂ surfaces. Technische Universität München

L. Sauerland: Machine-learned interatomic potentials for the syngas conversion on Rhodium. Ludwig-Maximilians-Universität München

N. Bergmann: Investigation of oxidized Cu surfaces using Gaussian Approximation Potentials. Technische Universität München