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.