Dresden 2026 – wissenschaftliches Programm

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SYAI: Symposium AI and Data Challenges behind Emerging Self-Driving Laboratories

SYAI 1: AI and Data Challenges behind Emerging Self-Driving Laboratories

SYAI 1.2: Hauptvortrag

Donnerstag, 12. März 2026, 10:00–10:30, HSZ/AUDI

Digital Catalysis - AI for Experiment Planning and Control — •Christoph Scheurer — Fritz-Haber-Institut der MPG, Berlin

Self-driving laboratories (SDLs) epitomize a cutting-edge integration of machine learning with laboratory automation. Operating in active learning loops, SDLs use machine learning algorithms to plan experiments that are then executed by increasingly automated (robotic) modules. Here, I will present our perspective on emerging SDLs for accelerated discovery and process optimization in heterogeneous catalysis. Drawing on recent work in catalyst discovery, kinetic modelling, and catalyst degradation studies, I will argue against the paradigm of full automation and the objective of keeping the human out of the loop. Analysis of the associated workflows indicates that crucial advances will stem from establishing fast proxy experiments, re-engineering existing apparatuses and measurement protocols, and developing modelling approaches with real-time capabilities. Industrially relevant use cases will also require humans to remain in the loop for continuous decision-making, mandating explainable AI. In turn, active learning algorithms must be advanced to flexibly accommodate adaptations of the design space and variations in information content and noise in the acquired data.

Keywords: catalysis; kinetics; machine-learning; design of experiment