The research in the Theory Department focuses on a quantitative modeling of materials properties and functions, and in particular on processes in working catalysts and energy conversion devices. For this we advance and employ predictive-quality multiscale models, advanced data science techniques and machine learning, thereby straddling the frontiers of physics, chemistry, computing sciences, as well as materials science and engineering.
Electroreduction of CO2 in a Non-aqueous Electrolyte ─ The Generic Role of Acetonitrile
T. Mairegger et al.
ACS Catal. 13, 5780 (2023)
Ångstrom-Depth Resolution with Chemical Specificity at the Liquid-Vapor Interface
R. Dupuy et al.,
Phys. Rev. Lett. 130, 156901 (2023)
Exploring catalytic reaction networks with machine learning
J.T. Margraf, H. Jung, C. Scheurer, and K. Reuter
Nature Catal. (accepted)
Graphene at Liquid Copper Catalysts: Atomic-Scale Agreement of Experimental and First-Principles Adsorption Height
H. Gao et al.
Adv. Sci. (2022)
Human- and machine-centred designs of molecules and materials for sustainability and decarbonization
J. Peng et al.
Nature Rev. Mat. (2022)
Ab Initio Thermodynamic Stability of Carbide Catalysts under Electrochemical Conditions
H. Li and K. Reuter
ACS Catal. 12, 10506 (2022)
Predicting Binding Motifs of Complex Adsorbates using Machine Learning with a Physics-Inspired Graph Representation
W. Xu, M. Andersen, and K. Reuter
Nature Comp. Sci. 2, 443 (2022).
Selectivity Trends and Role of Adsorbate–Adsorbate Interactions in CO Hydrogenation on Rhodium Catalysts
M. Deimel, H. Prats, M. Seibt, K. Reuter, and M. Andersen
ACS Catal. 12, 7907 (2022)
Field Effects at Protruding Defect Sites in Electrocatalysis at Metal Electrodes?
S. D. Beinlich, N. G. Hörmann, and K. Reuter
ACS Catal., 12, 6143 (2022)
A Model-Free Sparse Approximation Approach to Robust Formal Reaction Kinetics
F. Felsen, K. Reuter und C. Scheurer
Chem. Eng. J. 433, 134121 (2022).