Phosphorescent Material Search Using a Combination of High-Throughput Evaluation and Machine Learning

High-throughput experiments including combinatorial chemistry are useful for generating large amounts of data within a short period of time. Machine learning can be used to predict the regularity of a response variable using a statistical model of a data set. Because a combination of these methods can accelerate the material development, we applied such a combination to a search of semiconducting thin films prepared on an Eu and Dy codoped SrAl2O4-based phosphorescent material to improve the lifetime of its afterglow. Oxide targets MgO, GeO2, Ga2O3, ZnO, Bi2O3, Ta2O5, TiO2, and Y2O3 were deposited to form a thin film on a SrAl2O4 substrate as a combinatorial library with a systematical change in these ratios. The sample was calcined under several conditions, and a data set of 800 examples was obtained using a high-throughput evaluation. The 800 examples were then randomly divided into training and test data sets. The lifetime of the afterglow was interpolated through machine learning using the film thickness of each element and the calcined condition of the training data set as explanatory variables. The accuracy of the interpolation was evaluated using a correlation coefficient and the root mean squared error of the predicted values with respect to the experimental values of the test data set. As a result, it was found that a MgO thin film is effective at improving the lifetime of the afterglow and that its optimum condition is a film thickness of approximately 100 nm with calcination at 400–600 °C in air.