Working in the context of computational chemistry, I focus on method development for fast yet reliable total energy estimatiosn. In my view, it is key to include as many possible applications in the design of the methods in order to avoid having convoluted hard-to-use methods tailored only to an individual problem. This is why I enjoy including concepts from a range of my previous work (global cluster optimisation, large-scale classical molecular dynamics, and development for Density Functional Theory code) in current projects.
Currently, I am affiliated with the group of Prof. von Lilienfeld at University of Basel working on predictions of total electronic energies based on perturbation of a molecular core potential or machine learning.
- Alchemical energy predictions, with Prof. von Lilienfeld, University of Basel
- Machine learning of transition states, same
- Transition metal oxide water interfaces, with Prof. Blumberger, University College London and Dr. Rosso, Pacific Northwest National Laboratory
- Fluorinated organic compounds in membrane environments, Prof. Sebastiani, Martin-Luther-Universität Halle
- Global optimisation algorithms for atomic potentials, Prof. Sebastiani, Freie Universität Berlin