Theory Department

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

 

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. (in press)

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. (in press)

Active discovery of organic semiconductors

Christian Kunkel, Johannes T. Margraf, Ke Chen, Harald Oberhofer, and Karsten Reuter
Nature Commun. 12, 2422 (2021)

True nature of the transition-metal carbide/liquid interface determines its reactivity

C. Griesser, H. Li, E. M. Wernig, D. Winkler, N. Shakibi Nia, T. Mairegger, T. Götsch, T. Schachinger, A. Steiger-Thirsfeld, S. Penner, D. Wielend, D. Egger, C. Scheurer, K. Reuter, and J. Kunze-Liebhäuser
ACS Catal. 11, 4920 (2021)

Data-efficient machine learning for molecular crystal structure prediction

Simon Wengert, Gábor Csányi, Karsten Reuter, Johannes T. Margraf
Chemical Science, Advance Article (2021)

Pure non-local machine-learned density functional theory for electron correlation

Johannes T. Margraf, and Karsten Reuter
Nature Commun. 12, 344 (2021)

Data-driven descriptor engineering and refined scaling relations for predicting transition metal oxide reactivity

Wenbin Xu, Mie Andersen, and Karsten Reuter
ACS Catal. 11, 734 (2021)

Active site representation in first-principles microkinetic models: data-enhanced computational screening for improved methanation catalysts

Martin Deimel, Karsten Reuter, and Mie Andersen
ACS Catal. 10, 13729 (2020)

IrO2 Surface complexions identified through machine learning and surface investigations

Jakob Timmermann et al.
Phys. Rev. Lett. 125, 206101 (2020).

Theory Department News

The ions in a solid state battery need to travel through multiple material interfaces which comes along with several challenges. A team of the Fritz-Haber-Institute, the TU Munich and the Forschungszentrum Jülich now shows that nano-scale coating at these interfaces may in fact stabilize the battery. more

Scientists from the Theory Department of the Fritz-Haber Institute in Berlin and Technical University of Munich use machine learning to discover suitable molecular materials. To deal with the myriad of possibilities for candidate molecules, the machine decides for itself which data it needs. more

Prof. Dr. Karsten Reuter, director of the Department of Theory, is co-coordinator of a new priority program funded by the German Research Foundation (DFG). The program, based at Westfälische Wilhelms-Universität (WWU) Münster, investigates the use and development of machine learning for molecular applications. more

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