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MM: Fachverband Metall- und Materialphysik

MM 6: Phase Transformations

MM 6.1: Vortrag

Montag, 17. März 2025, 15:45–16:00, H10

Phase transitions in 2D halide perovskites using machine learned potentials — •Erik Fransson, Julia Wiktor, and Paul Erhart — Chalmers University of Technology, Gothenburg, Sweden

Hybrid halide perovskites are a promising class of materials for various applications, including high-efficiency solar cells, lasers, and light-emitting diodes. So-called two-dimensional (2D) halide perovskites, composed of a small number of perovskite layers stacked on top of each other and separated by organic cations that act as spacers, have much improved stability compared to their 3D counterparts. Here, we focus on the prototypical perovskite methylammonium lead halide (MAPI), and demonstrate that the dimensionality of these 2D materials and the choice of organic linker molecules can have a strong impact on phase transitions in these systems. This is investigated through large-scale molecular dynamics simulations using machine-learned potentials. We analyze the phase transition temperatures and characteristics with varying numbers of perovskite layers to understand how the transition properties change as a function of the system's dimensionality. For a larger number of perovskite layers, the 3D bulk phase transition temperature is recovered, whereas, for only a few perovskite layers, the phase transition temperature shifts up by about 100 K. Additionally, we observe surface effects, such as the surface layers (closest to the organic linker) exhibiting stronger octahedral tilting and undergoing phase transitions at higher temperatures (about 100 K) compared to the interior bulk layers.

Keywords: Phase transitions; Perovskites; 2D; Machine learned potentials; Molecular dynamics simulations