Theory Department
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.
Recent publications
Operando Characterization and Molecular Simulations Reveal the Growth Kinetics of Graphene on Liquid Copper During Chemical Vapor Deposition
V. Rein et al.
ACS Nano (in press)
Discovering High Entropy Alloy Electrocatalysts in Vast Composition Spaces with Multiobjective Optimization
W. Xiu, E. Diesen, T. He, K. Reuter and J.T. Margraf
J. Am. Chem. Soc. 146, 7698 (2024)
First Step of the Oxygen Reduction Reaction on Au(111): A Compu- tational Study of O2 Adsorption at the Electrified Metal/Water Interface
A.M. Dudzinski, E. Diesen, H.H. Heenen, V.J. Bukas, and K. Reuter
ACS Catal. 13, 12074 (2023)
Physics-inspired machine learning of localized intensive properties
K. Chen, C. Kunkel, B. Cheng, K. Reuter, and J.T. Margraf
Chem. Sci. 14, 4913 (2023)
Machine-learning driven global optimization of surface adsorbate geometries
H. Jung, L. Sauerland, S. Stocker, K. Reuter, and J.T. Margraf
npj Comp. Mater. 9, 114 (2023)
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. 6, 112 (2023)
