Publications of Karsten Reuter

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

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

Türk, H.: Active Site Generation and Deactivation of the Air Electrode in High Temperature Solid Oxide Cells. Technische Universität München

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

Xu, W.: Tailoring complexity for catalyst discovery using physically motivated machine learning. Technische Universität München

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Landini, E., K. Reuter and H. Oberhofer: Machine-learning Based Screening of Lead-free Halide Double Perovskites for Photovoltaic Applications., in press.