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

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, focusing on low...

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Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 26736 - 13
Main Authors: Sabagh Moeini, Alireza, Shariatmadar Tehrani, Fatemeh, Naeimi-Sadigh, Alireza
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 05.11.2024
Nature Publishing Group
Nature Portfolio
Subjects:
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639/705
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Approximation
Artificial intelligence
Band gap prediction
Double perovskites
Elemental features
Feature ranking
Humanities and Social Sciences
II-VI semiconductors
Layered perovskites
Learning algorithms
Machine learning
multidisciplinary
Neural networks
Predictions
Science
Science (multidisciplinary)
Symmetry
Valence
Band gap prediction
Layered perovskites
Machine learning
Elemental features
Double perovskites
Feature ranking
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary: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, focusing on low-symmetry double and layered structures. We employ Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost) to predict both direct and indirect band gaps. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) metrics. Our results reveal SVR as the most effective general model for predicting band gaps in both double and layered perovskites. Interestingly, for double perovskites specifically, XGBoost achieves even higher accuracy when incorporating derivative discontinuity as a feature. Feature importance analysis identifies the standard deviation of valence charges (“Valence (std)”) as the most critical factor for band gap prediction across all studied perovskites. This research demonstrates the potential of ML for efficient and accurate band gap prediction in complex perovskites, accelerating material discovery efforts.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-77081-7