Aarhus University Seal / Aarhus Universitets segl

Catalytic structure-activity relationships with machine learning

In the group we use various computational modelling and simulation tools based on quantum mechanics and statistical physics to study catalytic reactions at different kinds of surfaces. We develop relationships between the composition and structure of the surface and the catalytic activity and selectivity to specific reaction products, including detailed mechanistic understanding about the dominant reaction pathways and rate-determining steps. As such, we work at the interface between physics and chemistry, with recent machine learning and compressed sensing-based methods applied having their origin in computer science.  

Open positions

We currently do not have any open position.

Students interested in doing their BSc or MSc projects in the group are invited to contact Mie Andersen.

New research projects

In spring 2021 two new projects began. Click on the images below to learn more.  

Recent publications

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

Wenbin Xu, Mie Andersen, 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, Mie Andersen

ACS Catal. 10, 13729 (2020) 

Self-activation of copper electrodes during CO electro-oxidation in alkaline electrolyte

Andrea Auer, Mie Andersen, Eva-Maria Wernig, Nicolas G. Hörmann, Nico Buller, Karsten Reuter, Julia Kunze-Liebhäuser

Nat. Catal. 3, 797 (2020)

Interface between graphene and liquid Cu from molecular dynamics simulations

Juan Santiago Cingolani, Martin Deimel, Simone Köcher, Christoph Scheurer, Karsten Reuter, Mie Andersen

J. Chem. Phys. 153, 074702 (2020)