RUPP Matthias

About

I am a researcher working on data-driven methods for materials science. After studying computer science, I obtained my PhD in bioinformatics at the University of Frankfurt in 2009. I then worked as a postdoctoral researcher on cheminformatics at the Helmholtz centre in Munich, machine learning at the Technical University of Berlin, computer-assisted drug design at ETH Zurich, and physical chemistry at the University of Basel. At the Fritz Haber Institute of the Max Planck Society in Berlin, I led the research group on Machine Learning for Materials. I was a faculty member at the Department of Computer and Information Science of the University of Konstanz, Germany. In industry, I worked as an Applied Scientist for Citrine Informatics, a start-up working on active learning for industrial materials discovery and optimisation, and acted as Senior Scientific Advisor for Quantum Generative Materials (GenMat).

Mission

I lead the PROMAR (process modelling, automation, and robotisation) group at LIST. The PROMAR group develops data-driven methods to solve problems in materials science and adjoining fields. Bridging the divide between experiments and simulations, it pioneers the accelerated discovery and optimisation of novel materials, chemicals, and their processes through automated experimental platforms and advanced simulations. The group contributes to solutions for real-world challenges, such as energy storage and green processes for industrial applications. The group focuses on accurate, computationally efficient machine-learning surrogate models of expensive-to-evaluate functions, such as results of wet-lab experiments and electronic-structure calculations. This includes machine-learning interatomic potentials for accurate all-atom simulations at unprecedented time and length scales, as well as multi-objective surrogate-based (Bayesian) optimisation for property prediction and the design of chemicals and materials. These methods drive semi-autonomous laboratories for materials discovery, such as novel water-splitting catalysts, the analysis and optimisation of vapour deposition processes, and the accurate prediction of thermal transport in nano-electronic devices.

Skills and expertise

• Molecular Dynamics Simulations
• Materials Informatics
• Machine Learning

Projects

• SIMINDENT (FNR BRIDGES 2025)
• UFP4MP (FNR CORE 2025)