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Beyond Tolerance Factor: Using Deep Learning for Prediction Formability of ABX3 Perovskite Structures
Author(s) -
Fedorovskiy Alexander E.,
Queloz Valentin I. E.,
Nazeeruddin Mohammad Khaja
Publication year - 2021
Publication title -
advanced theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.068
H-Index - 17
ISSN - 2513-0390
DOI - 10.1002/adts.202100021
Subject(s) - formability , perovskite (structure) , task (project management) , artificial intelligence , deep learning , computer science , materials science , engineering , systems engineering , metallurgy , chemical engineering
Deep learning (DL) is a modern powerful instrument for multiple purposes, including classification. In this study, this technique is applied to the task of perovskites formability. A commonly known perovskite dataset is used to try to make an instrument superior to the ‘classic’ geometric approach. The authors found that the resulting models allow the finding of inaccuracies in the data and can successfully forecast perovskite formability with an accuracy of over 98% for the best case.