  In recent years, GW-BSE has been proven to be extremely successful in
studying the quasiparticle (QP) bandstructures and excitonic effects in the
optical properties of materials. However, the massive computational cost
associated with such calculations restricts their applicability in
high-throughput material discovery studies. Recently, we developed a Python
workflow package, $py$GWBSE, to perform high-throughput GW-BSE simulations. In
this work, using $py$GWBSE we create a database of various QP properties and
excitonic properties of over 350 chemically and structurally diverse materials.
Despite the relatively small size of the dataset, we obtain highly accurate
supervised machine learning (ML) models via the dataset. The models predict the
quasiparticle gap with an RMSE of 0.36 eV, exciton binding energies of
materials with an RMSE of 0.29 eV, and classify materials as high or low
excitonic binding energy materials with classification accuracy of 90%. We
exemplify the application of these ML models in the discovery of 159
visible-light and 203 ultraviolet-light photoabsorber materials utilizing the
Materials Project database.
