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
Out of the Crystalline Comfort Zone: Sampling the Initial Oxide Formation At Cu(111)
F. Riccius, N. Bergmann, H.H. Heenen, K. Reuter
Adv. Sci. e13878 (2025)
Learning Crystallographic Disorder: Bridging Prediction and Experiment in Materials Discovery
K.S. Jakob, A. Walsh, K. Reuter, J.T. Margraf
Adv. Mater. e14226 (2025)
Machine Learning the Energetics of Electrified Solid-Liquid Interfaces
N. Bergmann, N. Bonnet, N. Marzari, K. Reuter, N.G. Hörmann
Phys. Rev. Lett. 135, 146201 (2025)
Pushing charge equilibration-based machine learning potentials to their limits
M. Vondrák, K. Reuter, J.T. Margraf
npj Computational Materials 11, 288 (2025)
Spin-informed universal graph neural networks for simulating magnetic ordering
W. Xu et al.
PNAS 122, e2422973122 (2025)
Electron Spillover into Water Layers: A Quantum Leap in Understanding Capacitance Behavior
L. Li, T. Eggert, K. Reuter, N.G. Hörmann
JACS 147, 22778 (2025)
Origin of Electrocatalytic Selectivity during the Oxygen Reduction Reaction on Au(111)
E. Diesen, A.M. Dudzinski, K. Reuter, V.J. Bukas
ACS Catalysis 15, 5403 (2025)
Automatic Process Exploration through Machine Learning Assisted Transition State Searches
K.C. Lai, P. Poths, S. Matera, C. Scheurer, K. Reuter
Physical Review Letters 134, 096201 (2025)
