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https://doi.org/10.1016/j.jallcom.2017.03.148

has been cited by the following article:

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

  AUTHORS: Yuri Tamamoto, Mariko Murayama, Xiwei Zhao

  KEYWORDS: Machine Learning, Phosphor, TiO2

  JOURNAL NAME: Optics and Photonics Journal, Vol.12 No.6, June 28, 2022

  ABSTRACT: 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.