  High-throughput $ab$ $initio$ calculations are the indispensable parts of
data-driven discovery of new materials with desirable properties, as reflected
in the establishment of several online material databases. The accumulation of
extensive theoretical data through computations enables data-driven discovery
by constructing machine learning and artificial intelligence models to predict
novel compounds and forecast their properties. Efficient usage and extraction
of data from these existing online material databases can accelerate the next
stage materials discovery that targets different and more advanced properties,
such as electron-phonon coupling for phonon-mediated superconductivity.
However, extracting data from these databases, generating tailored input files
for different $ab$ $initio$ calculations, performing such calculations, and
analyzing new results can be demanding tasks. Here, we introduce a software
package named "HTESP" (High-Throughput Electronic Structure Package) written in
Python and Bash languages, which automates the entire workflow including data
extraction, input file generation, calculation submission, result collection
and plotting. Our HTESP will help speed up future computational materials
discovery processes.
