Akademik Veri Yönetim Sistemi
Materials Science in Semiconductor Processing, cilt.200, 2025 (SCI-Expanded)
In this study, we investigate the nonlinear optical rectification (NOR) in GaAs/AlGaAs Conical Core/Shell Quantum Dots under varying temperature conditions, utilizing compact density matrix formalism. The energy levels and wave functions are computed by solving the Schrödinger equation with the Finite Element Method (FEM) within the framework of the effective mass approximation (EMA). The objective of the present study is to develop an accurate and efficient method for modelling and predicting the NOR coefficient, taking into account the influence of pressure effect on the quantum dot system. To achieve this, we apply a range of machine learning algorithms, specifically, four ML models were studied; Artificial Neural Networks (ANN), Decision Trees (DT), Gradient Boosting (GB) and Recurrent Neural Networks (RNN). Among these, the Decision Tree model exhibits exceptional prediction performance, achieving R2 = 0.9984, MSE = 10−3, and MAE = 9.1 × 10−3 at a fixed value of the pressure at 20 kbar. The importance of this work lies in its potential to provide valuable insights for neither designing advanced quantum dot-based optoelectronic devices, such as infrared detectors and photonic components, where temperature-dependent NOR are properties crucial for performance optimization. Furthermore, the application of machine learning techniques in this context offers a promising approach for efficient and accurate modelling of complex quantum systems, facilitating the development of future quantum technologies.