Machine learning-enhanced band gaps prediction for low-symmetry double and layered perovskites

Machine learning-enhanced band gaps prediction for low-symmetry double and layered perovskites

Abstract Density functional theory (DFT) calculations are widely used for material property prediction, but their computational cost can hinder the discovery of novel perovskites. This work explores machine learning (ML) as a faster alternative for predicting band gaps in complex perovskites, focusi...

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Bibliographic Details
Main Authors: Alireza Sabagh Moeini, Fatemeh Shariatmadar Tehrani, Alireza Naeimi-Sadigh
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Scientific Reports
Subjects:
Double perovskites
Layered perovskites
Band gap prediction
Machine learning
Feature ranking
Elemental features
Online Access:https://doi.org/10.1038/s41598-024-77081-7
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https://doi.org/10.1038/s41598-024-77081-7

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