Regensburg 2025 – wissenschaftliches Programm

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HL: Fachverband Halbleiterphysik

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

Montag, 17. März 2025, 09:30–13:00, H17

The focus session highlights recent advances of machine learning concepts for the characterization of semiconductors. It particularly spotlights ML-driven advances in the prediction of key semiconductor properties, such as band gaps, dielectric constants and optical spectra, which are critical for the design of next-generation energy materials.

The focus session is organized by Erich Runge (TU Ilmenau).

	09:30	HL 3.1	Hauptvortrag: Alexandria Database - Improving machine-learning models in materials science through large datasets — •Jonathan Schmidt, Tiago Cerqueira, Aldo Romero, Silvana Botti, and Miguel Marques
	10:00	HL 3.2	Hauptvortrag: Generative Models on the Rise - Which one shall I pick for my Inverse Design Problem? — •Hanna Türk, Elisabetta Landini, Christian Kunkel, Patricia König, Christoph Scheurer, Karsten Reuter, and Johannes Margraf
	10:30	HL 3.3	Hauptvortrag: Machine-learning accelerated prediction of two-dimensional conventional superconductors — Thalis H. B. da Silva, Théo Cavignac, Tiago F. T. Cerqueira, •Haichen Wang, and Miguel A. L. Marques
	11:00		15 min. break
	11:15	HL 3.4	Hauptvortrag: Machine Learning for Design, Understanding, and Discovery of (Semiconducting) Materials — •Pascal Friederich
	11:45	HL 3.5	Hauptvortrag: OptiMate: Artificial intelligence for optical spectra — •Malte Grunert and Max Großmann
	12:15	HL 3.6	Mechanical Properties of Hybrid Perovskites study using explainable Machine Learning — •Yuxuan Yao, Dan Han, and Harald Oberhofer
	12:30	HL 3.7	Exploring Strongly Anharmonic Thermal Insulators with Machine-Learned Interatomic Potential using an Active Learning Scheme — •Shuo Zhao, Kisung Kang, and Matthias Scheffler
	12:45	HL 3.8	Learning an effective Hamiltonian for large-scale electronic-structure calculations — •Martin Schwade and David A. Egger