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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 1.4: Hauptvortrag

Donnerstag, 12. März 2026, 11:15–11:45, HSZ/AUDI

Machine Learning for Autonomous Optimization and Discovery of Materials — •Pascal Friederich — Karlsruhe Institute of Technology, Karlsruhe, Germany

Machine learning can accelerate the screening, design, and discovery of new molecules and materials in multiple ways, e.g. by virtually predicting properties of molecules and materials, by extracting hidden relations from large amounts of simulated or experimental data, or even by interfacing machine learning algorithms for autonomous decision-making directly with automated high-throughput experiments. In this talk, I will focus on our research activities on the use of machine learning for automated data analysis and autonomous decision-making in self-driving labs [1,2]. I will discuss extensions of Bayesian optimization to extend the autonomous decision-making process to more complex experimental and computational self-driving labs, as well as possibilities to include machine learning based recommendations already in the process of designing the experiments [3].

[1] Wu et al., Science 386, 6727 (2024), Inverse design workflow discovers hole-transport materials tailored for perovskite solar cells

[2] Marwitz et al., arXiv:2506.16824 (2025), Predicting New Research Directions in Materials Science using Large Language Models and Concept Graphs

[3] Jenewein et al., J. Mater. Chem. A 12, 3072-3083 (2024), Navigating the unknown with AI: multiobjective Bayesian optimization of non-noble acidic OER catalysts

Keywords: Machine Learning; Self-Driving Labs; Autonomous Decision-Making; Bayesian Optimization; Materials Discovery