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.