On Friday, Nikolaj Rønne successfully defended his PhD thesis titled "Data-Driven Methods for Material Discovery". Nikolaj will continue with InterCat as a postdoc in the Bjørk Hammer group.

In 2020, Nikolaj started his PhD, and has since become an important part of the group, strengthening collaborations between theory and experiment and sharing passion for science at the Forskningens Døgn.

During his PhD studies, Nikolaj 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 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.

Congratulations Nikolaj and well done. We are very happy that you are continuing with InterCat.