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

ABSTRACT: 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, pyGWBSE, to perform high-throughput GW-BSE simulations. In this work, using pyGWBSE 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 RMS

SUBMITTER: Biswas T 

PROVIDER: S-EPMC11912357 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature