Fig. 1: Motivation and methodology.

a The progress in solid oxide conductivities over decades moving diagonally up toward higher conductivities at lower temperatures. The data has been taken from references^{69,70,116,117}. We aim to accelerate this process through machine learning (ML). b The flow chart for the methodology adopted in this work. Total conductivity and charge carrier data from literature and 111 features from open databases are used to train a regressor ML model for total conductivity, to screen perovskites for high conductivities. These are then tested for stability based on energy above hull and tolerance factor. The stable perovskites are then classified for majority charge carrier using the charge carrier data from literature and 112 features (+ total conductivity), resulting in the prediction of new perovskite chemistries for different charge carriers. c Periodic table¹¹⁸ (adapted) showing the complexity of screened perovskite (ABO₃) chemistries for conductivity. All the A, B, and M (which is A- or B-site dopant) elements considered are shown. The elements have been chosen depending on the stability for AO and A₂O₃ type oxides and BO₂ and B₂O₃ type oxides for type I (AO + BO₂) and type II (A₂O₃ + B₂O₃) perovskites with M₂O₃ or MO type dopants, respectively.