| Abstract Scope |
The compositional and structural variety inherent to oxide perovskites and their fascinating properties spawn wide-ranging applications. The band gap in these materials can be optimally controlled by varying the composition. Here, we use a novel hierarchical screening process, wherein we build four machine learning (ML) models, designed to be applied sequentially to a very large chemical space, to yield novel double oxide perovskite chemistries that are predicted to be experimentally formable, thermodynamically stable and are insulator materials with a significant band gap. We identify a tractable set of promising candidates with high confidence and computationally verify their stability and band gaps. Our multi-step hierarchical screening approach, which may be generalized to investigate other classes of materials in addition to those examined here, provides further impetus to the application of physics-based ML models to the discovery of novel functional materials. We also apply this approach to the case of binary selenides. |