During his PhD studies, Nikolaj Rønne developed methods for the autonomous discovery of novel materials exploiting data-driven learning techniques. A key achievement of his work is the introduction of an innovative diffusion-based generative method, which allows the inverse design of stable surface materials. This method represents a substantial advancement in the field and opens up new possibilities for surface material discovery.

Nikolaj Rønne also applied Bayesian statistical learning methods and neural networks to model the potential energy surfaces of materials, further supporting the discovery capabilities. His research focused on surface-supported systems, including silver oxides relevant for industrial catalysis and graphene-supported molecular assemblies of carbon dioxide, which are of interest for understanding interstellar conditions necessary for life on Earth. These studies have deepened the theoretical understanding of these systems and contributed valuable insights to the field of material science.

The PhD study was completed at Department of Physics and Astronomy, Faculty of Natural Sciences, Aarhus University.

This summary was prepared by the PhD student.

Time: Friday, 25 October 2024 at 14.00
Place: Building 1525, room 626, Institute of Physics and Astronomy, Aarhus University
Title of PhD thesis: Data-Driven Methods for Material Discovery

Contact information: Nikolaj Rønne, e-mail: nronne@phys.au.dk, tel.: +45 41434949

Members of the assessment committee:
Professor Johannes T. Margraf, Physical Chemistry V: Theory and Machine Learning, University of Bayreuth, Germany.

Junior professor Julia Westermayr, University of Leipzig, Germany.

Associate Professor Jill Miwa (chair), Department of Physics and Astronomy, Aarhus University, Denmark.

Main supervisor:
Professor Bjørk Hammer (chair), Department of Physics and Astronomy, Aarhus University, Denmark.

Co-supervisor:

Language: The PhD dissertation will be defended in English

The defence is public.
The PhD thesis is available for reading at the Graduate School of Natural Sciences/GSNS, Ny Munkegade 120, building 1521, 8000 Aarhus C