Martin Kuban defended his doctoral thesis on April 15, 2025. He is a member of the Theoretical Solid-State Physics group at Humboldt-Universität zu Berlin. His work focused on extracting comparable “fingerprints” for materials from heterogeneous data sources in order to identify compounds which may have similar properties. As part of this work, he developed MADAS, a python framework providing a modular and extendable interface for similarity calculations in material science.
His contributions to subproject A3 in FONDA include automating this technique as a workflow to calculate similarity between different instances of the same material in an open source repository, where its features have been calculated using different sets of parameters. This allows for the automated detection of parameters which produce reliable results, and identification of those which introduce artifacts.
His excellent work and presentation earned the grade summa cum laude – with highest honors. Congratulations Martin!