A public database of thermoelectric materials and system-identified material representation for data-driven discovery

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.