Rapid discovery of new Eu2+-activated phosphors with a designed luminescence color using a data-driven approach

Abstract

For rapid and efficient development of new phosphors, a suitable method that proposes promising candidates is expected to focus time-consuming trial-and-error experiments. A data-driven approach to discover new phosphor materials with a designed luminescence color is demonstrated in this paper. To screen compounds for a desirable luminescence color, a machine learning model has been developed for predicting emission peak wavelengths from a dataset composed of 129 Eu²⁺-activated phosphors. General-purpose compositional and structural features are used to represent host compounds of phosphors. Bootstrap aggregation with the gradient boosted regression trees method is adopted to obtain high predictive performance and to avoid overfitting. The predictive performance of the machine learning model is estimated to be 25 nm of mean absolute error (MAE) and 33 nm of root mean squared error (RMSE) by 10-fold cross validation. To discover new green-emitting Eu²⁺-activated phosphors, twenty candidate compounds have been selected to have predicted emission peak wavelengths of about 500–550 nm from a materials database, and the candidates have been synthesized and characterized by experiments. Three new Eu²⁺-activated phosphors, Li₂Ca₄Si₄O₁₃:Eu²⁺, Na₂Ca₂Si₂O₇:Eu²⁺, and SrLaGaO₄:Eu²⁺, successfully show green or blue-green emissions as designed.