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Renewable energy sources are of great interest to combat global warming, yetpromising sources like photovoltaic (PV) cells are not efficient and cheapenough to act as an alternative to traditional energy sources. Perovskite hashigh potential as a PV material but engineering the right material for aspecific application is often a lengthy process. In this paper, ABO3 typeperovskites' formability is predicted and its crystal structure is classifiedusing machine learning with high accuracy, which provides a fast screeningprocess. Although the study was done with solar-cell application in mind, theprediction framework is generic enough to be used for other purposes.Formability of perovskite is predicted and its crystal structure is classifiedwith an accuracy of 98.57% and 90.53% respectively using Random Forest after5-fold cross-validation. Our machine learning model may aid in the accelerateddevelopment of a desired perovskite structure by providing a quick mechanism toget insight into the material's properties in advance.

ABO3 Perovskites' Formability Prediction and Crystal Structure Classification using Machine Learning