ML-aided High-throughput screening for Novel Oxide Perovskite Discovery

This Jupyter notebook is a tutorial that illustrate's the basic approach of conceptualizing the problem at different abstraction levels and translate from one abstraction level to the others hierarchically. This approach is illustrated by the example of wide band gap oxide perovskites. The tool will sequentially search a very large domain space of single and double oxide perovskites to identify candidates that are likely to be formable, thermodynamically stable, exhibit insulator nature and have a wide band gap. To this end, the tool will build four machine learning (ML) models: three classification and one regression model using experimental and DFT-calculated training data.