This presentation was held in context of the Interdisciplinary Colloquium on Digitalisation of Research of the Leibniz Science Campus "DiTraRe" (Digital Transformation of Research). AI and machine learning methods are playing an increasingly important role in science. In materials science and chemistry, they 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 automated data analysis and autonomous decision-making in self-driving labs [1], as well as our work on predicting new research directions in materials science using large language models and concept graphs [2]. [1] Wu et al., Science 386, 6727 (2024), https://www.science.org/doi/abs/10.1126/science.ads0901[2] Marwitz et al., Nature Machine Intelligence 8, 535–544 (2026), https://doi.org/10.1038/s42256-026-01206-y