[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119155-en":3,"doc-seo-119155-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119155,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning aided parameter analysis in Perovskite X-ray Detector","Machine learning aided parameter analysis is applied to perovskite X-ray detectors, where crystal lattice and carrier dynamics jointly determine sensitivity and detection limits. The study builds a training database for ML models by correlating 15 intrinsic halide perovskite properties with detector performance. Results identify key drivers, showing band gap primarily influenced by the B-site metal atomic number, while lattice length parameter b most strongly affects the carrier mobility-lifetime product (μτ). Experimental generation of an m-F-PEA2PbI4 detector further confirms model accuracy and supports follow-up work on random forest regression for device applications.","Machine learning aided parameter analysis in Perovskite X-ray Detector Bobo Zhang 1†, Endai Huang2,3†, Xinyi Du4,5, Xiaokang Ma6, Lu Zhang 1, Jiaxue You7*,  \nAlex K.Y. Jen7* and Shengzhong (Frank) Liu 1,8,9*  \n1Key Laboratory of Applied Surface and Colloid Chemistry, Ministry of Education; Shaanxi Key Laboratory for Advanced Energy Devices; Shaanxi Engineering Lab for Advanced Energy Technology; Institute for Advanced Energy Materials; School of Materials Science and Engineering, Shaanxi Normal University, Xi’an 710119, China. 2Research Institute of Medical and Biological Engineering, Ningbo University, Zhejiang 315211, China  \n3Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR 999077, China  \n4Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore.  \n5 Solar Energy Research Institute of Singapore (SERIS), National University of Singapore, Singapore, Singapore.  \n6 State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, PR China.  \n7Department of Materials Science and Engineering, Hong Kong Institute for Clean Energy City University of Hong Kong, Hong Kong SAR 999077, China  \n8Dalian National Laboratory for Clean Energy; iChEM, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.  \n9University of the Chinese Academy of Sciences, Beijing 100039, China.  \n†: BZ and EH contributed equally.  \nCorresponding authors*: JY: [jiaxuyou@cityu.edu.hk](jiaxuyou@cityu.edu.hk), AJ: [alexjen@cityu.edu.hk](alexjen@cityu.edu.hk), SL: [szliu@dicp.ac.cn](szliu@dicp.ac.cn).  \nABSTRACT: 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  \nrelationship 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.  \nKEYWORDS:  \nX-ray detector, machine learning, lattice parameters, carrier dynamics, dark current.  \n1. Introduction  \nX-ray detectors play a vital role in various fields such as nuclear physics and technology, medical diagnostics, non-destructive testing, security inspections, astronomical observations, and high-energy physics research1. Halide perovskites have emerged as a promising candidate for X-ray detection due to their exceptional photoelectric properties, including a large atomic number for high absorption coefficient, a large carrier mobility-lifetime product (μτ) for efficient charge collection, and adjustable band gaps leading to low leakage currents2, 3. The performance evaluation of X-ray detectors relies heavily on parameters such as sensitivity, detection limit, and dark current4. For applications like medical imaging, maintaining exceptional sensitivity and a low detection limit is crucial to minimize radiation exposure. Additionally, these detectors must exhibit dark current densities below 1 nA cm−2 to uphold high detection quantum efficiency and dynamic range.5 Researchers have concentrated on material design, particularly focusing on A-site cations6-13, B-site ions 14-19, and X-site anions regulation20, 21.  \nUtilizing three-dimensional (3D) perovskite materials has enabled X-ray detectors  \nto achieve outstanding performance metrics22, alt","cbCaipmK4ggLPvo9","https://ap.wps.com/l/cbCaipmK4ggLPvo9","pdf",1286797,1,20,"English","en",105,"# Introduction\n## Perovskite X-ray detector importance and key performance metrics\n## Materials factors affecting performance\n## Challenges in understanding parameter relationships\n## Role of machine learning in materials design","[{\"question\":\"Which perovskite intrinsic properties are used to predict X-ray detector performance?\",\"answer\":\"The study uses 15 intrinsic properties of halide perovskite materials and correlates them with detector performance metrics via machine learning.\"},{\"question\":\"What factors most strongly influence band gap and carrier mobility-lifetime product?\",\"answer\":\"The band gap is mainly influenced by the atomic number of the B-site metal, while the lattice length parameter b has the greatest impact on the carrier mobility-lifetime product (μτ).\"},{\"question\":\"How is the ML model accuracy validated experimentally?\",\"answer\":\"An X-ray detector based on m-F-PEA2PbI4 is generated in the experiment, which further verifies the accuracy of the ML models.\"}]","Machine learning aided parameter analysis in Perovskite X-ray Detector | 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perovskite intrinsic properties are used to predict X-ray detector performance?","Question",{"text":75,"@type":76},"The study uses 15 intrinsic properties of halide perovskite materials and correlates them with detector performance metrics via machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What factors most strongly influence band gap and carrier mobility-lifetime product?",{"text":80,"@type":76},"The band gap is mainly influenced by the atomic number of the B-site metal, while the lattice length parameter b has the greatest impact on the carrier mobility-lifetime product (μτ).",{"name":82,"@type":73,"acceptedAnswer":83},"How is the ML model accuracy validated experimentally?",{"text":84,"@type":76},"An X-ray detector based on m-F-PEA2PbI4 is generated in the experiment, which further verifies the accuracy of the ML 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