Machine learning models for accurately predicting properties of CsPbCl3 Perovskite quantum dots
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Nature Research
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Perovskite Quantum Dots (PQDs) have a promising future for several applications due to their unique properties. This study investigates the effectiveness of Machine Learning (ML) in predicting the size, absorbance (1S abs) and photoluminescence (PL) properties of CsPbCl<inf>3</inf> PQDs using synthesizing features as the input dataset. The study employed ML models of Support Vector Regression (SVR), Nearest Neighbour Distance (NND), Random Forest (RF), Gradient Boosting Machine (GBM), Decision Tree (DT) and Deep Learning (DL). Although all models performed highly accurate results, SVR and NND demonstrated the best accurate property prediction by achieving excellent performance on the test and training datasets, with high R2, low Root Mean Squared Error (RMSE) and low Mean Absolute Error (MAE) metric values. Given that ML is becoming more superior, its ability to understand the QDs field could prove invaluable to shape the future of nanomaterials designing. © 2025 Elsevier B.V., All rights reserved.
Açıklama
Anahtar Kelimeler
Perovskite, Nanomaterial, Perovskite, Quantum Dot, Article, Controlled Study, Decision Tree, Deep Learning, Machine Learning, Mean Absolute Error, Normal Human, Photoluminescence, Prediction, Random Forest, Root Mean Squared Error, Support Vector Machine
Kaynak
Scientific Reports
WoS Q Değeri
Scopus Q Değeri
Q1
Cilt
15
Sayı
1
