Beyond Tolerance Factor: Using Deep Learning for Prediction Formability of ABX3 Perovskite Structures

被引：7

作者：

Fedorovskiy, Alexander E. ^{[1
]}

Queloz, Valentin I. E. ^{[1
]}

Nazeeruddin, Mohammad Khaja ^{[1
]}

机构：

[1] Ecole Polytech Fed Lausanne EPFL, Inst Chem Sci & Engn, Grp Mol Engn Funct Mat, EPFL ISIC Valais, CH-1951 Sion, Switzerland

来源：

ADVANCED THEORY AND SIMULATIONS | 2021年 / 4卷 / 05期

基金：

瑞士国家科学基金会;

关键词：

crystal structures; deep learning; formability prediction; perovskites; tolerance factor;

D O I：

10.1002/adts.202100021

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：6

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