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.
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).
Implicit Solvation Methods for Catalysis at Electrified Interfaces
S. Ringe, N. G. Hörmann, H. Oberhofer, and K. Reuter
Chem. Rev. (in press)
Subgroup Discovery Points to the Prominent Role of Charge Transfer in Breaking Nitrogen Scaling Relations at Single-Atom Catalysts on VS2
H. Li, Y. Liu, K. Chen, J. T. Margraf, Y. Li, and K. Reuter
ACS Catal. 11, 7906 (2021)
Nano-Scale Complexions Facilitate Li Dendrite-Free Operation in LATP Solid-State Electrolyte
S. Stegmaier, 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
Adv. Eng. Mater. 11, 2100707 (2021)
Active discovery of organic semiconductors
C. Kunkel, J. T. Margraf, K. Chen, H. Oberhofer, and K. Reuter
Nature Commun. 12, 2422 (2021)