Regensburg 2025 – scientific programme

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HL: Fachverband Halbleiterphysik

HL 3: Focus Session: Machine Learning of semiconductor properties and spectra

HL 3.4: Invited Talk

Monday, March 17, 2025, 11:15–11:45, H17

Machine Learning for Design, Understanding, and Discovery of (Semiconducting) Materials — •Pascal Friederich — Karlsruher Institut für Technologie

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 graph neural networks for property prediction [1] and understanding of structure-property relations [2], as well as on the use of machine learning for automated data analysis and autonomous decision-making in self-driving labs, especially in the area of semiconductor optimization for photovoltaics [3,4].

[1] Reiser et al., Communications Materials 3 (1) (2022), https://www.nature.com/articles/s43246-022-00315-6

[2] Teufel et al., xAI (2023), https://arxiv.org/abs/2211.13236

[3] Wu et al., JACS 2023, https://pubs.acs.org/doi/full/10.1021/jacs.3c03271

[4] Wu et al., Science 2024

Keywords: Machine Learning; Property Prediction; Materials Design; Self-Driving Labs; Autonomous Discovery