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
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)
Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis
C. Scheurer and K. Reuter
Nature Catalysis 8, 13 (2025)
ML-Accelerated Automatic Process Exploration Reveals Facile O-Induced Pd Step-Edge Restructuring on Catalytic Time Scales
P. Poths et al.
ACS Catalysis 15, 514 (2025)
Deciphering the Capacitance of the Pt(111)/Water Interface: A Micro- to Mesoscopic Investigation by AIMD and Implicit Solvation
L. Li, K. Reuter, N.G. Hörmann
ACS Electrochemistry 1, 186 (2025)
Materials Acceleration Platforms (MAPs) Accelerating Materials Research and Development to Meet Urgent Societal Challenges
S.P. Stier et al.
Advanced Materials 36, 2407791 (2024)
