  Renewable energy sources are of great interest to combat global warming, yet
promising sources like photovoltaic (PV) cells are not efficient and cheap
enough to act as an alternative to traditional energy sources. Perovskite has
high potential as a PV material but engineering the right material for a
specific application is often a lengthy process. In this paper, ABO3 type
perovskites' formability is predicted and its crystal structure is classified
using machine learning with high accuracy, which provides a fast screening
process. Although the study was done with solar-cell application in mind, the
prediction framework is generic enough to be used for other purposes.
Formability of perovskite is predicted and its crystal structure is classified
with an accuracy of 98.57% and 90.53% respectively using Random Forest after
5-fold cross-validation. Our machine learning model may aid in the accelerated
development of a desired perovskite structure by providing a quick mechanism to
get insight into the material's properties in advance.
