Application of Machine Learning Techniques for the Optimization of Synthesis Parameters of GaN Quantum Dots for Utilization in Ultraviolet Photodetectors
Faculty of Basic Science, Shahid Sattari University of Aeronautical Sciences and Technology, Tehran, Iran.
Abstract
Accurate ultraviolet (UV) photodetection is a critical issue in the development of optoelectronic technologies. Conventional semiconductor materials commonly used in UV photodetectors suffer from limitations in terms of response speed, cost, and fabrication complexity. In contrast, UV photodetectors based on quantum dots (QDs), owing to their unique optical and electronic properties, can potentially overcome these limitations. Nevertheless, the synthesis of these nanoparticles is typically based on trial-and-error approaches. In this study, using machine learning models and information extracted from the scientific literature, we investigate and predict the most influential variables governing the synthesis of gallium nitride (GaN) QDs applicable to UV photodetectors. The results obtained from the AdaBoost algorithm indicate that the Ga/N molar ratio, the aluminum-to-metal flux ratio, time, and growth temperature are the most important variables affecting the bandgap of GaN QDs. Moreover, the AdaBoost algorithm is employed to predict the optimal values of the key reaction variables within desired GaN QD bandgap ranges.
Abedi Ravan,B. and Daalwand,B. (2026). Application of Machine Learning Techniques for the Optimization of Synthesis Parameters of GaN Quantum Dots for Utilization in Ultraviolet Photodetectors. (e251). Aerospace Defense, 4(4), e251
MLA
Abedi Ravan,B. , and Daalwand,B. . "Application of Machine Learning Techniques for the Optimization of Synthesis Parameters of GaN Quantum Dots for Utilization in Ultraviolet Photodetectors" .e251 , Aerospace Defense, 4, 4, 2026, e251.
HARVARD
Abedi Ravan B., Daalwand B. (2026). 'Application of Machine Learning Techniques for the Optimization of Synthesis Parameters of GaN Quantum Dots for Utilization in Ultraviolet Photodetectors', Aerospace Defense, 4(4), e251.
CHICAGO
B. Abedi Ravan and B. Daalwand, "Application of Machine Learning Techniques for the Optimization of Synthesis Parameters of GaN Quantum Dots for Utilization in Ultraviolet Photodetectors," Aerospace Defense, 4 4 (2026): e251,
VANCOUVER
Abedi Ravan B., Daalwand B. Application of Machine Learning Techniques for the Optimization of Synthesis Parameters of GaN Quantum Dots for Utilization in Ultraviolet Photodetectors. Aerospace Defense, 2026; 4(4): e251.
