Publications of Karsten Reuter

Li, H., Y. Liu, K. Chen, J. Margraf, Y. Li and K. Reuter: Subgroup Discovery Points to the Prominent Role of Charge Transfer in Breaking Nitrogen Scaling Relations at Single-Atom Catalysts on VS₂. ACS Catalysis 11 (13), 7906–7914 (2021).

Margraf, J.T. and K. Reuter: Pure non-local machine-learned density functional theory for electron correlation. Nature Communications 12, 344 (2021).

Staacke, C., H. Heenen, C. Scheurer, G. Csányi , K. Reuter and J. Margraf: On the Role of Long-Range Electrostatics in Machine-Learned Interatomic Potentials for Complex Battery Materials. ACS Applied Energy Materials 4 (11), 12562–12569 (2021).

Stegmaier, S., R. Schierholz, I. Povstugar, J. Barthel, S.P. Rittmeyer, S. Yu, S. Wengert, S. Rostami, H. Kungl, K. Reuter, R.-A. Eichel and C. Scheurer: Nano-Scale Complexions Facilitate Li Dendrite-Free Operation in LATP Solid-State Electrolyte. Advanced Energy Materials 11 (26), 2100707 (2021).

Timmermann, J., Y. Lee, C. Staacke, J. Margraf, C. Scheurer and K. Reuter: Data-Efficient Iterative Training of Gaussian Approximation Potentials: Application to Surface Structure Determination of Rutile IrO₂ and RuO₂. The Journal of Chemical Physics 155 (24), 244107 (2021).

Tsakonas, C., A.C. Manikas, M. Andersen, M. Dimitropoulos, K. Reuter and C. Galiotis: In situ kinetic studies of CVD graphene growth by reflection spectroscopy. Chemical Engineering Journal 421 (1), 129434 (2021).

Türk, H., F. Schmidt, T. Götsch, F. Girgsdies, A. Hammud, D. Ivanov, I.C. Vinke, L.G.J. de Haart, R.-A. Eichel, K. Reuter, R. Schlögl, A. Knop-Gericke, C. Scheurer and T. Lunkenbein: Complexions at the Electrolyte/Electrode Interface in Solid Oxide Cells. Advanced Materials Interfaces 8 (18), 2100967 (2021).

Weindl, R., R. Khare, L. Kovarik, A. Jentys, K. Reuter, H. Shi and J.A. Lercher: Zeolite-Stabilized Di- and Tetranuclear Molybdenum Sulfide Clusters Form Stable Catalytic Hydrogenation Sites. Angewandte Chemie International Edition 60 (17), 9301–9305 (2021).

Wengert, S., G. Csányi, K. Reuter and J. Margraf: Data-efficient machine learning for molecular crystal structure prediction. Chemical Science 12 (12), 4536–4546 (2021).

Xu, W., M. Andersen and K. Reuter: Data-Driven Descriptor Engineering and Refined Scaling Relations for Predicting Transition Metal Oxide Reactivity. ACS Catalysis 11 (2), 734–742 (2021).

Auer, A., M. Andersen, E.-M. Werning, N.G. Hörmann, N. Buller, K. Reuter and J. Kunze-Liebhäuser: Self-activation of copper electrodes during CO electro-oxidation in alkaline electrolyte. Nature Catalysis 3 (10), 797–803 (2020).

Cheng, B., R.-R. Griffiths, S. Wengert, C. Kunkel, T. Stenczel, B. Zhu, V.L. Deringer, N. Bernstein, J.T. Margraf, K. Reuter and G. Csanyi: Mapping Materials and Molecules. Accounts of Chemical Research 53 (9), 1981–1991 (2020).

Cingolani, J.S., M. Deimel, S. Köcher, C. Scheurer, K. Reuter and M. Andersen: Interface between graphene and liquid Cu from molecular dynamics simulations. The Journal of Chemical Physics 153 (7), 074702 (2020).

Deimel, M., K. Reuter and M. Andersen: Active Site Representation in First-Principles Microkinetic Models: Data-Enhanced Computational Screening for Improved Methanation Catalysts. ACS Catalysis 10 (22), 13729–13736 (2020).

Ghan, S., C. Kunkel, K. Reuter and H. Oberhofer: Improved Projection-Operator Diabatization Schemes for the Calculation of Electronic Coupling Values. Journal of Chemical Theory and Computation 16 (12), 7431–7443 (2020).

Hörmann, N.G., N. Marzari and K. Reuter: Electrosorption at metal surfaces from first principles. npj Computational Materials 6, 136 (2020).

Jung, H., S. Stocker, C. Kunkel, H. Oberhofer, B. Han, K. Reuter and J.T. Margraf: Size-Extensive Molecular Machine Learning with Global Representations. ChemSystemsChem 2 (4), e1900052 (2020).

Li, H. and K. Reuter: Active-Site Computational Screening: Role of Structural and Compositional Diversity for the Electrochemical CO₂ Reduction at Mo Carbide Catalysts. ACS Catalysis 10 (20), 11814–11821 (2020).

Panosetti, C., A. Engelmann, L. Nemec, K. Reuter and J.T. Margraf: Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression. Journal of Chemical Theory and Computation 16 (4), 2181–2191 (2020).

Stocker, S., G. Csányi, K. Reuter and J.T. Margaf: Machine learning in chemical reaction space. Nature Communications 11, 5505 (2020).