Optimization of Crystal Structure Based on Experimentally Obtained XRD Patterns for Fluorescence of Sm³⁺-Doped TiO₂ Thin Films by Machine Learning

作     者：Yuri Tamamoto Mariko Murayama Xiwei Zhao Yuri Tamamoto;Mariko Murayama;Xiwei Zhao

作者机构：Department of Physics Tokyo University of Science Tokyo Japan Research Institute of Industrial Technology Toyo University Saitama Japan Department of Materials Science and Engineering Xihua University Chengdu China 

出 版 物：《Optics and Photonics Journal》 (光学与光子学期刊（英文）)

年 卷 期：2022年第12卷第6期

页      面：147-155页

学科分类：081704[工学-应用化学] 07[理学] 08[工学] 0817[工学-化学工程与技术] 0703[理学-化学] 070301[理学-无机化学] 

主　　题：Machine Learning Phosphor TiO₂ 

摘      要：The luminescence intensity of rare-earth ion-doped luminescent materials is closely related to the configuration of the anions around the rare-earth ions added to the host material and the lattice defects. And it is expected that this information will be reflected in the XRD pattern. In this study, the lumines-cence data and XRD patterns of Sm-doped TiO2 accumulated by our group are used to construct a model to predict the integrated luminescence intensity. The model was confirmed to be able to predict the integrated luminescence in-tensity with high accuracy. Furthermore, we found that the integrated lumi-nescence intensity of this system is closely related to the change in the position of the peak on the (200) plane of TiO2.