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Abstract Developing inorganic phosphor with desired properties for light-emitting diode application has traditionally relied on time-consuming and labor-intensive material development processes. Moreover, the results of material development research depend significantly on individual researchers’ intuition and experience. Thus, to improve the efficiency and reliability of materials discovery, machine learning has been widely applied to various materials science applications in recent years. However, the prediction capabilities of machine learning methods fundamentally depend on the quality of the training datasets. In this work, we constructed a high-quality and reliable dataset that contains experimentally validated inorganic phosphors and their optical properties, sourced from the literature on inorganic phosphors. Our dataset includes 3952 combinations of 21 dopant elements in 2238 host materials from 553 articles. The dataset provides material information, optical properties, measurement conditions for inorganic phosphors, and meta-information. Among the preliminary machine learning results, the essential properties of inorganic phosphors, such as maximum Photoluminescence (PL) emission wavelength and PL decay time, show overall satisfactory prediction performance with coefficient of determination ( $$R^2$$ R 2 ) scores of 0.7 or more. We also confirmed that the measurement conditions significantly improved prediction performance.

Abstract Thermoelectric materials have received much attention as energy harvesting devices and power generators. However, discovering novel high-performance thermoelectric materials is challenging due to the structural diversity and complexity of the thermoelectric materials containing alloys and dopants. For the efficient data-driven discovery of novel thermoelectric materials, we constructed a public dataset that contains experimentally synthesized thermoelectric materials and their experimental thermoelectric properties. For the collected dataset, we were able to construct prediction models that achieved R 2-scores greater than 0.9 in the regression problems to predict the experimentally measured thermoelectric properties from the chemical compositions of the materials. Furthermore, we devised a material descriptor for the chemical compositions of the materials to improve the extrapolation capabilities of machine learning methods. Based on transfer learning with the proposed material descriptor, we significantly improved the R 2-score from 0.13 to 0.71 in predicting experimental ZTs of the materials from completely unexplored material groups.

Abstract Dopants play an important role in synthesizing materials to improve target materials properties or stabilize the materials. In particular, the dopants are essential to improve thermoelectic performances of the materials. However, existing machine learning methods cannot accurately predict the materials properties of doped materials due to severely nonlinear relations with their materials properties. Here, we propose a unified architecture of neural networks, called DopNet, to accurately predict the materials properties of the doped materials. DopNet identifies the effects of the dopants by explicitly and independently embedding the host materials and the dopants. In our evaluations, DopNet outperformed existing machine learning methods in predicting experimentally measured thermoelectric properties, and the error of DopNet in predicting a figure of merit (ZT) was 0.06 in mean absolute error. In particular, DopNet was significantly effective in an extrapolation problem that predicts ZTs of unknown materials, which is a key task to discover novel thermoelectric materials.