Publications of Karsten Reuter

Reuter, K.: First-principles statistical mechanics for oxidation catalysis. Habilitation, Freie Universität Berlin, Berlin (2005)

Huss, T.: Towards a universal machine learning interatomic potential for the xLi₂S-(100 - x)P₂S₅ material class. Master, Technische Universität, München (2022)

König, P.: Generative Adversarial Networks (GANs) for inverse design of RuO₂ surfaces. Master, Technische Universität, München (2022)

Bergmann, N.: Investigation of oxidized Cu surfaces using Gaussian Approximation Potentials. Master, Technische Universität, München (2021)

Sauerland, L.: Machine-learned interatomic potentials for the syngas conversion on Rhodium. Master, Ludwig-Maximilians-Universität, München (2021)

Jakob, K.; Reuter, K.; Margraf, J.: Universally Accurate or Specifically Inadequate? Stress-testing General Purpose Machine Learning Interatomic Potentials. (2025)

Müller, N. S.; Fellows, A. P.; John, B.; Naclerio, A. E.; Carbogno, C.; Gharagozloo-Hubmann, K.; Balaz, D.; Kowalski, R. A.; Heenen, H.; Scheurer, C. et al.; Reuter, K.; Caldwell, J. D.; Wolf, M.; Kidambi, P. R.; Thämer, M.; Paarmann, A.: Full Crystallographic Imaging of Hexagonal Boron Nitride Monolayers with Phonon-Enhanced Sum-Frequency Microscopy. (2025)

Samtsevych, A.; Song, Y.; van der Heide, T.; Aradi, B.; Hourahine, B.; Maurer, R. J.; Reuter, K.; Scheurer, C.; Panosetti, C.: DSKO: dancing through DFTB parameterization. (2025)

Landini, E.; Reuter, K.; Oberhofer, H.: Machine-learning Based Screening of Lead-free Halide Double Perovskites for Photovoltaic Applications. (2022)

Neumann, F.; Margraf, J.; Reuter, K.; Bruix, A.: Interplay between shape and composition in bimetallic nanoparticles revealed by an efficient optimal-exchange optimization algorithm. (2021)

Klag, L.; Gläser, R.; Krewer, U.; Reuter, K.; Grunwaldt, J.-D.: Special Collection: Catalysts and Reactors under Dynamic Conditions for Energy Storage and Conversion. ChemCatChem, e202401191 (2024)