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Anion-exchange enabled tuning of caesium lead mixed-halide perovskites for high-energy radiation detection
Matula, Radovan ; Friák, Martin (oponent) ; Dvořák, Petr (vedoucí práce)
Lead halide perovskites (LHPs) with their unprecedented functional qualities which are only enhanced by the simple band gap tuning, have taken the world of semiconductors by storm. The process of anion exchange, possible even post-synthesis, allows for band gap tuning of LHPs, resulting in lead mixed-halide perovskites (LMHPs), thus expanding their potential for applications, notably in tuneable detectors. The widespread adoption of LMHPs is, however, hindered by their chemical instability, which leads to halide segregation in the material, seriously inhibiting reliable operation of any LMHP-based device. Understanding the kinetics of the halide segregation over extended periods remains a challenge, motivating the use of theoretical simulations like Monte Carlo (MC) methods. Yet, MC simulations rely on well-defined potential energy surfaces (PES), typically derived from computationally intensive density functional theory (DFT) calculations. In this thesis, we propose a novel approach for constructing well-defined PES from high-fidelity DFT data with fraction of the computational load. Utilizing activation-relaxation technique noveau (ARTn) motivated searches for transition points in the PES combined with state-of-the-art machine learning approaches, we aim to to significantly reduce computational costs. Additionally, employing classical theory, we assess the detection capabilities of selected LMHPs.
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