ABO3 Perovskites' Formability Prediction and Crystal Structure Classification using Machine Learning
This work addresses the need for accelerated material development in renewable energy applications, such as photovoltaics, by providing a generic prediction framework, though it is incremental in applying existing methods to this domain.
The paper tackles the problem of predicting ABO3 perovskites' formability and classifying their crystal structure using machine learning, achieving high accuracies of 98.57% and 90.53% respectively to enable fast screening for materials like solar cells.
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.