Issue 29, 2024

Prediction of metastable energy level distribution of D3+ (D = Cr and Fe) doped phosphors based on machine learning

Abstract

The energy level transitions in phosphor materials critically determine their emission characteristics, and accurately predicting the energy level distribution of ions in these materials is critical for determining their luminescence behavior. However, reliance on multiple experimental methods to determine energy level distributions is inefficient, consuming both time and resources. There is an urgent need for a rapid and accurate method to predict the energy level distribution of ions in crystals. This paper employs regression models based on machine learning to propose a method for predicting the energy level distribution rules of Cr3+ and Fe3+ in various doped crystals, and identifies the position and distribution patterns of these levels in different doped crystals, as well as their impact on luminescence characteristics. Furthermore, a dataset detailing the energy level distributions of Cr3+ and Fe3+ doped into different phosphor materials was established. Eight machine learning regression algorithms were selected for model construction, and a comprehensive evaluation and comparison of these algorithms were conducted. The results demonstrate that robust regression delivers the best overall performance. Using trained models, predictions were made for the 2E and 4T1 energy levels in new Cr3+ and Fe3+ doped phosphor materials. The prediction errors of the optimal algorithms for these materials were all in the range of about 1%, with the best prediction error at just 0.0056%. This study introduces an innovative approach for predicting and optimizing the energy level structures and luminescence properties of phosphor materials.

Graphical abstract: Prediction of metastable energy level distribution of D3+ (D = Cr and Fe) doped phosphors based on machine learning

Article information

Article type
Paper
Submitted
26 मई 2024
Accepted
17 जून 2024
First published
20 जून 2024

J. Mater. Chem. C, 2024,12, 10889-10902

Prediction of metastable energy level distribution of D3+ (D = Cr and Fe) doped phosphors based on machine learning

J. Li, J. Sun, Y. Wang and X. Wang, J. Mater. Chem. C, 2024, 12, 10889 DOI: 10.1039/D4TC02168A

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements