Publications of Karsten Reuter

Andersen, M.; Reuter, K.: Adsorption Enthalpies for Catalysis Modeling through Machine-Learned Descriptors. Accounts of Chemical Research 54 (12), pp. 2741 - 2749 (2021)

Kunkel, C.; Margraf, J. T.; Chen, K.; Oberhofer, H.; Reuter, K.: Active discovery of organic semiconductors. Nature Communications 12, 2422 (2021)

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

Weindl, R.; Khare, R.; Kovarik, L.; Jentys, A.; Reuter, K.; Shi, H.; Lercher, J. A.: Zeolite-Stabilized Di- and Tetranuclear Molybdenum Sulfide Clusters Form Stable Catalytic Hydrogenation Sites. Angewandte Chemie 60 (17), pp. 9387 - 9391 (2021)

Griesser, C.; Li, H.; Werning, E.-M.; Winkler, D.; Nia, N. S.; Mairegger, T.; Götsch, T.; Schachinger, T.; Steiger-Thirsfeld, A.; Penner, S. et al.; Wielend, D.; Egger, D.; Scheurer, C.; Reuter, K.; Kunze-Liebhäuser, J.: True Nature of the Transition-Metal Carbide/Liquid Interface Determines Its Reactivity. ACS Catalysis 11 (8), pp. 4920 - 4928 (2021)

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

Hörmann, N.; Reuter, K.: Thermodynamic Cyclic Voltammograms Based on Ab Initio Calculations: Ag(111) in Halide-Containing Solutions. Journal of Chemical Theory and Computation 17 (3), pp. 1782 - 1794 (2021)

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

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

Götsch, T.; Türk, H.; Schmidt, F.; Vinke, I. C.; De Haart, L. G. J.; Schlögl, R.; Reuter, K.; Eichel, R.-A.; Knop-Gericke, A.; Scheurer, C. et al.; Lunkenbein, T.: Visualizing the Atomic Structure Between YSZ and LSM: An Interface Stabilized by Complexions? ECS Transactions 103 (1), pp. 1331 - 1337 (2021)

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

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

Timmermann, J.; Kraushofer, F.; Resch, N.; Li, P.; Wang, Y.; Mao, Z.; Riva, M.; Lee, Y.; Staacke, C.; Schmid, M. et al.; Scheurer, C.; Parkinson, G. S.; Diebold, U.; Reuter, K.: IrO₂ Surface Complexions Identified through Machine Learning and Surface Investigations. Physical Review Letters 125 (20), 206101 (2020)

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

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

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

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

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

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

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