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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Veröffentlicht in:	Scientific reports Jg. 14; H. 1; S. 26736 - 13
Hauptverfasser:	Sabagh Moeini, Alireza, Shariatmadar Tehrani, Fatemeh, Naeimi-Sadigh, Alireza
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 05.11.2024 Nature Publishing Group Nature Portfolio
Schlagworte:	639/301 639/705 639/766 639/925 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-Zugang:	Volltext
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Zusammenfassung:	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.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-024-77081-7