FHI Theory

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

 

First Step of the Oxygen Reduction Reaction on Au(111): A Compu- tational Study of O2 Adsorption at the Electrified Metal/Water Interface

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

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

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

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

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

Exploring catalytic reaction networks with machine learning

J.T. Margraf, H. Jung, C. Scheurer, and K. Reuter
Nature Catal. (accepted)

Graphene at Liquid Copper Catalysts: Atomic-Scale Agreement of Experimental and First-Principles Adsorption Height

Graphene at Liquid Copper Catalysts: Atomic-Scale Agreement of Experimental and First-Principles Adsorption Height

H. Gao et al.
Adv. Sci. (2022)

Human- and machine-centred designs of molecules and materials for sustainability and decarbonization

Human- and machine-centred designs of molecules and materials for sustainability and decarbonization

J. Peng et al.
Nature Rev. Mat. (2022)

Ab Initio Thermodynamic Stability of Carbide Catalysts under Electrochemical Conditions

Ab Initio Thermodynamic Stability of Carbide Catalysts under Electrochemical Conditions

H. Li and K. Reuter
ACS Catal. 12, 10506 (2022)

Predicting Binding Motifs of Complex Adsorbates using Machine Learning with a Physics-Inspired Graph Representation

Predicting Binding Motifs of Complex Adsorbates using Machine Learning with a Physics-Inspired Graph Representation

W. Xu, M. Andersen, and K. Reuter
Nature Comp. Sci. 2, 443 (2022). 

Selectivity Trends and Role of Adsorbate–Adsorbate Interactions in CO Hydrogenation on Rhodium Catalysts

Selectivity Trends and Role of Adsorbate–Adsorbate Interactions in CO Hydrogenation on Rhodium Catalysts

M. Deimel, H. Prats, M. Seibt, K. Reuter, and M. Andersen
ACS Catal. 12, 7907 (2022)

Theory Department News

Dr. Hemanth Somarajan Pillai is Awarded the Prestigious Alexander-von-Humboldt Fellowship for Postdoctoral Researchers

The Fritz Haber Institute is thrilled to announce that Dr. Hemanth Somarajan Pillai has secured a prestigious Alexander-von-Humboldt fellowship to continue his postdoctoral work in the group of Dr. Vanessa Jane Bukas. With his expertise in Density Functional Theory and Microkinetic Modelling, Dr. Pillai will contribute to advance the Theory Department´s understanding of electrocatalysts and their selectivity. more

Batteries go solar – Project <em>SolBat</em><br />funded by the Max Planck Foundation with around 3 Mio. Euros

Joint project between the Max Planck Institute for Solid State Research (Stuttgart) and the Fritz Haber Institute of the Max Planck Society (Berlin) explores new avenues to solar batteries and other optoionic technologies. more

Celebrating Excellence in Research: Dr. Elena Gelžinytė joins Institute´s Theory Department as Alexander-von-Humboldt Research Fellow

The Theory Department of the Fritz-Haber-Institut welcomes Dr. Elena Gelžinytė as the latest addition to its team. With her expertise in Machine Learning, she will complement the institute’s research and thereby advance our understanding of materials with unique light-matter interaction. more

Bane and boon of oxygen mediating the performance of nickel catalysts in dry reforming of methane

Catalysis is one of the key technologies in the chemical industry and has a wide-reaching impact on various aspects of our daily lives, including plastics manufacturing, drug synthesis, and production of both fertilizers and fuels. It is estimated that over 90% of chemical products are nowadays manufactured with the involvement of catalysis in at least one stage (Catal. Today, 2011, 163(1)). Catalysis is a complex process that relies on the precise structural control of several elements at the crossroads of phase (in-)stabilities. While long-term stable catalysts are indispensable in order to promote high-performance and efficient reactions, reactants undergo major chemical changes, leading to the formation of final and desired products. In heterogeneous catalysis, catalyst and reactants exist in different phases. more

Fritz-Haber-Institut PhD Students Shine at Prestigious CECAM/Psi-k Flagship School

Five PhD students from the Department of Theory at the Fritz-Haber-Institut der Max-Planck-Gesellschaft have made a significant impact at the recent CECAM/Psi-k Flagship School on Machine Learning Interatomic Potentials (ML-IP 2023). Patricia König, Kyeonghyeon Nam, Tabea Huss, Federico Civaia, and Sina Jennifer Ziegler secured their in-person spots at the highly competitive event held in November. more

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