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 SrAlO-based phosphorescent material to improve the lifetime of its afterglow.

Materials design and development of functional materials for industry.

It is now well recognized that we are witnessing a golden age of innovation with novel materials, with discoveries that are important for both basic science and industry. With the development of theory along with computing power, quantum materials design-the synthesis of materials with the desired properties in a controlled way via materials engineering on the atomic scale-is becoming a major component of materials research. Computational prediction based on first-principles calculations has helped to find an efficient way to develop materials that are much needed for industry, as we have seen in the successful development of visible-light sensitized photocatalysts and thermoelectric materials.