Dresden 2026 – wissenschaftliches Programm

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HL: Fachverband Halbleiterphysik

HL 4: Perovskite and Photovoltaics: Synthesis and Performance

HL 4.3: Vortrag

Montag, 9. März 2026, 10:15–10:30, POT/0251

Tuning Lead Halide Perovskite Nanocrystal Properties via Controllable Synthesis and Machine-Learning Insights — •Maximilian Dörfler, Leo Luber, and Alexander Urban — Nanospectroscopy Group, Nano-Institute Munich, Department of Physics, Ludwig-Maximilians-Universität München, Königinstraße 10, 80539 Munich, Germany

Halide perovskite nanocrystals (NCs) combine high photoluminescence efficiency with tunable emission, but achieving narrow linewidths requires tightly controlled, highly monodisperse synthesis. In this work, we perform in situ observations of CsPbBr3 NC formation under ambient conditions and systematically evaluate how synthesis parameters shape their optical response. By analyzing the evolving photoluminescence spectra, we assess how reaction time, dilution, and precursor-to-antisolvent ratios jointly influence crystal growth and resultant morphology. The in situ approach allows us to track nanocrystal growth pathways and provides the opportunity for real-time steering of the synthesis. Particular emphasis is placed on understanding the conditions under which the NCs adopt 2D versus 3D morphologies. In future work, we will employ machine-learning tools - specifically Bayesian optimization - to efficiently navigate the sparsely sampled synthesis parameter space. This data-driven strategy aims to identify regions that yield high quantum efficiencies and narrow linewidths, enabling rapid discovery of conditions that favor predominantly monodisperse nanocrystals or nanoplatelets with minimal experimental iteration.

Keywords: lead halide perovskites; machine-learning; in situ observation