Publications of Karsten Reuter

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

Jakob, K., K. Reuter and J. Margraf: Universally Accurate or Specifically Inadequate? Stress-testing General Purpose Machine Learning Interatomic Potentials., in press.

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

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

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

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