In our research we develop novel data-driven techniques to solve real materials design problems across scale in an actionable way. We use machine learning models as navigation systems for the chemical space. We do this in close collaboration with experimental partners and by working on several themes that are key for progress in the field.
We strive to make all our work practically useful by sharing code and data under permissive licenses in a reusable form. To us, computational work without open code is just mere advertisment. External link