Many-body physics and machine learning enabled discovery of promising solar materials

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, <i>py</i>GWBSE, to perform high-throughput GW-BSE simulations. In this work, using <i>py</i>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 <i>via</i> 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.