Machine learning aided parameter analysis in Perovskite X-ray Detector

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Auteurs principaux:	Zhang, Bobo, Huang, Endai, Du, Xinyi, Zhang, Lu, Ma, Xiaokang, You, Jiaxue
Format:	Preprint
Publié:	2024
Sujets:	Materials Science
Accès en ligne:	Acceder al recurso
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author	Zhang, Bobo Huang, Endai Du, Xinyi Zhang, Lu Ma, Xiaokang You, Jiaxue
author_facet	Zhang, Bobo Huang, Endai Du, Xinyi Zhang, Lu Ma, Xiaokang You, Jiaxue
contents	Many factors in perovskite X-ray detectors, such as crystal lattice and carrier dynamics, determine the final device performance (e.g., sensitivity and detection limit). However, the relationship between these factors remains unknown due to the complexity of the material. In this study, we employ machine learning to reveal the relationship between 15 intrinsic properties of halide perovskite materials and their device performance. We construct a database of X-ray detectors for the training of machine learning. The results show that the band gap is mainly influenced by the atomic number of the B-site metal, and the lattice length parameter b has the greatest impact on the carrier mobility-lifetime product (μτ). An X-ray detector (m-F-PEA)2PbI4 were generated in the experiment and it further verified the accuracy of our ML models. We suggest further study on random forest regression for X-ray detector applications.
format	Preprint
id	arxiv_https___arxiv_org_abs_2405_04729
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Machine learning aided parameter analysis in Perovskite X-ray Detector Zhang, Bobo Huang, Endai Du, Xinyi Zhang, Lu Ma, Xiaokang You, Jiaxue Materials Science Many factors in perovskite X-ray detectors, such as crystal lattice and carrier dynamics, determine the final device performance (e.g., sensitivity and detection limit). However, the relationship between these factors remains unknown due to the complexity of the material. In this study, we employ machine learning to reveal the relationship between 15 intrinsic properties of halide perovskite materials and their device performance. We construct a database of X-ray detectors for the training of machine learning. The results show that the band gap is mainly influenced by the atomic number of the B-site metal, and the lattice length parameter b has the greatest impact on the carrier mobility-lifetime product (μτ). An X-ray detector (m-F-PEA)2PbI4 were generated in the experiment and it further verified the accuracy of our ML models. We suggest further study on random forest regression for X-ray detector applications.
title	Machine learning aided parameter analysis in Perovskite X-ray Detector
topic	Materials Science
url	https://arxiv.org/abs/2405.04729

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