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.

Automated summary

This study constructed a public dataset to discover high-performance novel thermoelectric materials, and used machine learning methods to predict their thermoelectric properties, achieving improved prediction accuracy through transfer learning.