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
Embrace the Mess: Theory for Disordered Surfaces of Working Catalysts
K. Reuter
ACS Catalysis (2026)
General-purpose LLMs as constrained crystal composition generators
H. Oschinski, M. L. Ach, K. S. Jakob, C. Carbogno, K. Reuter
npj Computational Materials 12, 337 (2026)
From global optimization to transition state search: an automated workflow for surface reaction barriers
H. Jung et al.
npj Computational Materials 12, 300 (2026)
EZGA: An evolutionary structure exploration framework
J. M. Lombard et al.
Comput. Phys. Commun. (2026)
Multiscale Microscopy via Automation: Dual Magnification Environmental Scanning Electron Microscopy Imaging by Frame Alternation
M. Vuijk et al.
Adv. Mater. Technol. e00009 (2026)
Adaptive Experiment Planning for Inverse Design and Understanding: Synergistic Interactions as Key to Optimized Multi-Promoter Formulations
C.W.P. Pare et al.
ACS Catalysis, 16, 6798 (2026)
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)
