Importance of structural deformation features in the prediction of hybrid perovskite bandgaps

Given the surging growth of artificial-intelligence-inspired computational methods in materials science, experimental laboratories around the globe have become open to adopting data-driven approaches for materials discovery. The field witnesses emerging machine-learning models trained over databases, of which data are collected from high-throughput experimentation or first-principles calculation. Here, we address the impediment of constructing a highly accurate predictor for perovskite bandgap when the inorganic network undergoes the deformation. The predictor is trained on a dataset of first-principles calculations of pure and mixed-cation hybrid perovskites. We investigate the impact of the inclusion/exclusion of structural deformation features by training the model carefully. A high level of accuracy could be achieved with a scrupulous investigation of the input features. Our analysis emphasizes how important the feature selection is for the construction of the predictive model as we challenge the robustness of our machine learning predictor in a lab validation setup.