  Since the implementation of the Materials Genome Project by the Obama
administration in the United States, the development of various computational
materials databases has fundamentally expanded the choices of industries such
as materials and energy. In the field of thermoelectric materials, the
thermoelectric figure of merit ZT quantifies the performance of the material.
From the viewpoint of calculations for vast materials, the ZT values are not
easily obtained due to their computational complexity. Here, we show how to
build a database of thermoelectric materials based on first-principles
calculations for the electronic and heat transport of materials. Firstly, the
initial structures are classified according to the values of bandgap and other
basic properties using the clustering algorithm K-means in machine learning,
and high-throughput first principles calculations are carried out for
narrow-bandgap semiconductors which exhibiting potential thermoelectric
application. The present framework of calculations mainly includes deformation
potential module, electrical transport performance module, mechanical and
thermodynamic properties module. We have also set up a search webpage for the
calculated database of thermoelectric materials, providing searching and
viewing the related physical properties of materials. Our work may inspire the
construction of more computational databases of first-principle thermoelectric
materials and accelerate research progress in the field of thermoelectrics.
