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Hello! 👋 I’m Marcel, a doctoral student at the Fritz Haber Institute (in the NOMAD Laboratory) and TU Berlin (in the Machine Learning group).

Here’s the brief, jargon-y pitch for my research:

Computational quantum-mechanical modelling methods such as density functional theory can predict the properties of materials from first principles, but are limited by their high computational cost. In settings involving many simulations, machine learning can reduce these costs, often by orders of magnitude, by interpolating between reference calculations. In my doctoral research, I study how to design such fast surrogate models.

In practical terms, this means that I’m trying to understand how various machine-learning models can be used to approximate the relationship between the structure (i.e. the position of atoms and the unit cell) of a material and certain properties, in particular the energy and forces. This requires getting into the nitty-gritty of how these methods work, how physical requirements can be incorporated into them, and doing lots of parameter searches.

Here are some other places on the internet where you can find me:

If you have any questions, or just want to say hi, you should be able to reach me on twitter, or at

Thanks for stopping by! 🚀


These are the projects I’m working on:

I've also written some tutorials for the NOMAD Analytics Toolkit:

I also co-organise the Fritz Sessions, an intermittent series of lectures at the Fritz Haber Institute, covering topics related to the future.


More papers are on the way, I promise!