  Thermoelectric materials can achieve direct energy conversion between
electricity and heat, thus can be applied to waste heat harvesting and
solid-state cooling. The discovery of new thermoelectric materials is mainly
based on experiments and first-principles calculations. However, these methods
are usually expensive and time-consuming. Recently, the prediction of
properties via machine learning has emerged as a popular method in materials
science. Herein, we firstly did first-principles high-throughput calculations
for a large number of chalcogenides and built a thermoelectric database
containing 796 compounds. Many novel and promising thermoelectric materials
were discovered. Then, we trained four ensemble learning models and two deep
learning models to distinguish the promising thermoelectric materials from the
others for n type and p type doping, respectively. All the presented models
achieve classification accuracy higher than 85% and area under the curve (AUC)
higher than 0.9. Especially, the M3GNet model for n type data achieve accuracy,
precision and recall all higher than 90%. Our works demonstrate a very
efficient way of combining machine learning prediction and first-principles
high-throughput calculations together to accelerate the discovery of advanced
thermoelectric materials.
