Al-Ruqaishi, Zakiya and Ooi, Chong Heng Raymond (2024) Multilayer neural network models for critical temperature of cuprate superconductors. Computational Materials Science, 241. p. 113018. ISSN 0927-0256, DOI https://doi.org/10.1016/j.commatsci.2024.113018.
Full text not available from this repository.Abstract
A multi-layer neural network is used to extract the value of a superconductor's T-c of cuprate from two models. The first model extracts T-c from the structure's chemical composition whereas the second model extracts T-c from the structure's chemical composition and the lattice parameters. The back-propagation algorithm is used to find the empirical equation of T-c. It can calculate error signals and redistribute backward propagation signals. This paper studies four systems of cuprate superconductors: Y-Ba-Cu-O, Bi-Sr-Ca-Cu-O, Tl-Ba-Ca-Cu-O, and Hg-Ba-Ca-Cu-O. In the first model, the T-c of four high-temperature oxide superconductors are calculated as a function of eight parameters and one output, which is T-c. Although the same output is produced in the second model, it is produced as a function of eleven parameters. The eight parameters are superconductor type number (Bi2212, Bi2223, Hg1201, Hg1212, Hg1223, Y123, Y124, Y247, Tl1223, Tl2212, Tl2223), first component composition, second component composition, third component composition, fourth component composition, atomic number of doping type, doping composition, and oxygen composition of the first model. The previous parameters with the three lattice parameters a, b and c are used in the second model. The trained deep learning models have shown a high degree of performance in matching the trained distributions. After analysing the results, we deduce electronegativity plays an important role in increasing T-c of cuprate superconductors. Using the obtained T-c prediction model, the scope is expanded to include the eleven unexplored multi-element materials. Candidates for superconductors with a higher T-c that can be synthesized are proposed. By comparison with other models of machine learning, the suggested models in this paper give the highest T-c for predicting new cuprate superconductors.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Higher Education (MOHE) Malaysia, under Long-Term Research Grant Scheme (LRGS/1/2020/UM/01/5/1) |
| Uncontrolled Keywords: | High Temperature SuperConductor (HTSC); Critical temperature(T-c); Artificial Intelligence (AI); Artificial Neural Networks (ANN); Deep Learning (DL) |
| Subjects: | Q Science > QC Physics |
| Divisions: | Faculty of Science > Department of Physics |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 26 Sep 2024 04:05 |
| Last Modified: | 26 Sep 2024 04:05 |
| URI: | http://eprints.um.edu.my/id/eprint/45215 |
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