Prof. Dr. Johannes Margraf Joins University of Bayreuth as Professor

Prof. Dr. Johannes Margraf, a distinguished researcher and Group Leader at the Fritz-Haber-Institut der Max-Planck-Gesellschaft (FHI) in Berlin, has been appointed as a Professor at the University of Bayreuth. Prof. Dr. Margraf's association with the university officially commenced this September, marking a notable transition in his career.

Academic Journey

Prof. Dr. Margraf's academic journey has been marked by a series of prestigious positions and valuable contributions to the field of chemical machine learning and electronic structure theory. Prior to his current role at the University of Bayreuth, he held key positions at renowned institutions:

• Fritz-Haber-Institut der Max-Planck-Gesellschaft (FHI), Berlin: Group Leader,
• Technical University Munich: PostDoc and Group Leader,
• University of Florida: PostDoc.

His educational background includes a PhD in Physical and Computational Chemistry from FAU Erlangen-Nürnberg.

Research Focus

Prof. Dr. Margraf's research group, known as the "Data-Efficient Chemical Machine Learning" group, specializes in applying machine learning techniques to comprehend and predict chemical phenomena. What sets their work apart is their dedication to creating precise and data-efficient models that do not necessitate extensive reference datasets for training. This approach enables the application of their methods to a wide range of chemical problems, even in cases where substantial data resources are lacking.

One of their key objectives is to incorporate fundamental physical and chemical principles into their models, ensuring a deeper understanding of the underlying processes. This approach extends to electronic structure theory, where they explore the intersection of wavefunction and density functional methods, aiming to develop robust and accurate methodologies that overcome computational challenges.

Interview Highlights

In a recent interview, Prof. Dr. Margraf shared insights into his career and experiences:

1. How he came to FHI and when: Prof. Dr. Margraf joined FHI in October 2020 after working at the Technical University of Munich. His appointment at FHI came about when Karsten Reuter, the Director of the Theory Department at FHI, offered him a group leader position.
2. His work at FHI: Prof. Dr. Margraf's group at FHI focused on advancing machine learning in chemistry and catalysis. Notably, they integrate physical and chemical principles into their machine learning models to achieve data-efficient results.
3. Support from FHI and MPG: Prof. Dr. Margraf acknowledged the ideal research conditions provided by the Max Planck Society (MPG) and FHI, highlighting the invaluable support of colleagues in the Theory Department, under the leadership of Prof. Dr. Karsten Reuter.
4. Collaboration within his team and with Karsten Reuter: Prof. Dr. Margraf emphasized the collaborative culture at the Theory Department, which encouraged interdisciplinary cooperation. He also discussed his role as a group leader in supporting his team's development.
5. Professional takeaways: Prof. Dr. Margraf reflected on his transformative time at FHI, where he evolved into an independent researcher and gained extensive knowledge in catalysis and physical chemistry.
6. Future prospects: At the University of Bayreuth, Prof. Dr. Margraf is excited to establish a new research group focusing on Artificial Intelligence in Physico-Chemical Material Analysis. This career move allows him to continue his research indefinitely and explore new horizons.

The academic community eagerly anticipates the contributions Prof. Dr. Johannes Margraf will make at the University of Bayreuth, and his dedication to data-efficient chemical machine learning is expected to leave a lasting impact on the field. We at FHI have greatly enjoyed working with him and wish him the very best in his future endeavors.