Predictive machine learning approaches for perovskites properties using their chemical formula: towards the discovery of stable solar cells materials

In recent years, notable progress in computational density functional theory (DFT) has facilitated the collection of extensive datasets in the field of materials science. Machine learning is a crucial technique for effectively processing and analyzing these large datasets and accelerating the creation of new compounds. In this work, we employ the Extreme Gradient Boosting (XGBoost) classification algorithm to predict the crystal structure of 381 halides and oxide perovskites using 78 features. We achieved a classification accuracy of 76.62