Artificial Neural Network Approach for Quantum Defect Prediction in Alkali Atoms

Home Page
About
Submit A Journal
Submit A Conference
Submit Paper/Book
- Submit a Preprint
- Submit a Book
Contact

International Journal of Pure and Applied Sciences
Cilt: 11 Sayı: 2
Artificial Neural Network Approach for Quantum Defect Prediction in Alkali Atoms

Artificial Neural Network Approach for Quantum Defect Prediction in Alkali Atoms

Authors : Murat Kurt, Azmi Gençten

Pages : 382-392

Doi:10.29132/ijpas.1621829

View : 116 | Download : 260

Publication Date : 2025-12-29

Article Type : Research Paper

Abstract :In this study, an Artificial Neural Network (ANN) model was suggested and trained to predict the quantum defect values of alkali atoms. The dataset was divided into training and testing subsets with a % 60 – % 40, respectively. To prevent overfitting, the number of training epochs was limited to 250, and the learning rate was set to 0.25. The training process employed the Gradient Descent optimization algorithm for updating the network weights. Two different activation functions, ReLU and Swish, were utilized to evaluate their impact on prediction accuracy. The predicted quantum defect values obtained from the ANN were compared with corresponding experimental results to assess the model’s performance.
Keywords : Yapay sinir ağları, gradyan inişi, alkali atomlar

ORIGINAL ARTICLE URL

* There may have been changes in the journal, article,conference, book, preprint etc. informations. Therefore, it would be appropriate to follow the information on the official page of the source. The information here is shared for informational purposes. IAD is not responsible for incorrect or missing information.