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.