Regensburg 2025 – wissenschaftliches Programm

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CPP: Fachverband Chemische Physik und Polymerphysik

CPP 11: Hybrid and Perovskite Photovoltaics II

CPP 11.2: Vortrag

Montag, 17. März 2025, 16:30–16:45, H38

Tracking the Crystallization Pathway of Perovskite using Microscopy, Spectroscopy and Machine Learning — •Meike Kuhn¹, Milan Harth², Alessio Gagliardi², and Eva M. Herzig¹ — ¹Dynamik und Strukturbildung - Herzig Group, Universität Bayreuth, Universitätsstr. 30, 95447 Bayreuth, Germany — ²Simulation of Nanosystems for Energy Conversion, Technische Universität München, Hans-Piloty-Str. 1, 85748 Garching b. München, Germany

The interest in perovskite materials has grown significantly in recent years due to their diverse applications, with the crystallization process playing a crucial role in determining the final properties of perovskite films. Time-resolved techniques, such as microscopy and spectroscopy, enable detailed analysis of the various stages of perovskite formation.

In this study, we investigated the crystallization of methylammonium lead iodide (MAPbI3) blade coated from dimethylformamide (DMF), using a combined microscopy and spectroscopy approach. This approach allowed us to observe morphological and optical changes during intermediate phase formation and perovskite conversion, influenced by the addition of various additives. By applying a machine learning model to the microscopy data, we developed a predictive framework capable of estimating spectroscopic signals, thus enabling insights into physical properties with time-resolved microscopy.

Keywords: Perovskite; Machine Learning; blade coating; crystallization; additives