Designing Pr-based advanced photoluminescent materials using machine learning and density functional theory

This work presents a machine learning approach to predict novel perovskite oxide materials in the Pr-Al-O and Pr-Sc-O compound families with the potential for photoluminescence applications. The predicted materials exhibit a large bandgap and high Debye temperature, and have remained unexplored thus far. The predicted compounds (Pr \(_3\) ${}_3$ AlO \(_6\) ${}_6$ , Pr \(_4\) ${}_4$ Al \(_2\) ${}_2$ O \(_9\) ${}_9$ , Pr \(_3\) ${}_3$ ScO \(_6\) ${}_6$ and Pr \(_3\) ${}_3$ Sc \(_5\) ${}_5$ O \(_{12}\) ${}_{12}$ ) are screened using machine learning approach, which are then confirmed by density functional theory calculations. The study includes the calculation of the bandgap and density of states to determine electronic properties, and the optical absorption and emission spectra to determine optical properties. Mechanical stability of the predicted compounds, as demonstrated by satisfying the Born-Huang criterion. By combining machine learning and density functional theory, this work offers a more efficient and comprehensive approach to materials discovery and design.