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Welcome to the website of the Chair of Physical Chemistry V: Theory and Machine Learning. We use machine learning (ML) to understand and predict chemical phenomena, such as the nature of complex reaction networks or the properties of new molecules and materials.
Johannes MARGRAF, PostDoc Position | Cited by 3,463 | of Fritz Haber Institute of the Max Planck Society, Berlin (FHI) | Read 104 publications | Contact Johannes MARGRAF
4 de oct. de 2023 · Prof. Dr. Johannes Margraf Joins University of Bayreuth as Professor. October 04, 2023. 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.
Johannes T. Margraf Tuning the reorganization energy of electron transfer in supramolecular ensembles -- metalloporphyrin, oligophenylenevinylenes, and fullerene -- and the impact on electron transfer kinetics
We are delighted to welcome Prof. Dr. Johannes Margraf ( Chair of Artificial Intelligence in Physico-Chemical Material Analysis) as new member to the BayBatt. His chair deals with the theoretical modeling of functional energy materials as used in (photo)catalysis, batteries, and organic solar cells.
10 de mar. de 2023 · Dr. Johannes T. Margraf. First published: 10 March 2023. https://doi.org/10.1002/anie.202219170. Citations: 3. Tools. Graphical Abstract. Machine learning algorithms are currently emerging as powerful tools in all areas of science. This review covers atomistic machine learning approaches in chemistry beyond the conventional data-driven perspective.
Prof. Margraf has taken up a professorship from the Hightech Agenda Bayern AI competition and is based at the Bavarian Centre for Battery Technology (BayBatt) in Weihererstraße at the University of Bayreuth. Prof. Dr Johannes Margraf will give his inaugural lecture on 18 January 2024 at 5 p.m. in H14 (NWI).
