Machine learning prediction of electric field-dependent absorption coefficient in CdTe/CdS quantum dots

Ed-Dahmouny A., Zeiri N., Arraoui R., BAŞER P., Es-Sbai N., Sali A., ...Daha Fazla

Materials Today Physics, cilt.58, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 58
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.mtphys.2025.101851
  • Dergi Adı: Materials Today Physics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Absorption coefficient, Artificial neural network, CdTe/CdS, Core-shell quantum dot, Decision tree, Light gradient boosting machine, Machine learning, Oxide matrix, Random forest regression
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

We investigated the electric field-induced optical absorption coefficient in CdTe/CdS core-shell quantum dots embedded within titanium dioxide (TiO2) and silicon dioxide (SiO2) matrices. To model these changes, we employed a comparative approach, utilizing Artificial Neural Networks (ANN), Decision Trees (DT), Random Forest Regressors (RFR), and Light Gradient Boosting Machine (LightGBM) and comparing their predictions with numerical finite element method simulations. Our findings revealed that TiO2 embedding resulted in a redshift and amplitude increase of the absorption resonance, whereas SiO2 embedding or isolation caused a blueshift and amplitude decrease. Notably, the Random Forest Regressor exhibited the most accurate predictions, underscoring the effectiveness of machine learning in simulating and predicting the optical properties of quantum dot systems.