  We combine graph neural networks (GNN) with an inexpensive and reliable
structure generation approach based on the bond-valence method (BVM) to train
accurate machine learning models for screening 222,960 halide perovskites using
statistical estimates of the DFT/PBE formation energy (Ef), and the PBE and HSE
band gaps (Eg). The GNNs were fined tuned using domain adaptation (DA) from a
source model, which yields a factor of 1.8 times improvement in Ef and 1.2 -
1.35 times improvement in HSE Eg compared to direct training (i.e., without
DA). Using these two ML models, 48 compounds were identified out of 222,960
candidates as both stable and that have an HSE Eg that is relevant for
photovoltaic applications. For this subset, only 8 have been reported to date,
indicating that 40 compounds remain unexplored to the best of our knowledge and
therefore offer opportunities for potential experimental examination.
