Multilayer neural network models for critical temperature of cuprate superconductors
Document Type
Article
Publication Date
5-1-2024
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.
Keywords
High Temperature SuperConductor (HTSC), Critical temperature(T-c), Artificial Intelligence (AI), Artificial Neural Networks (ANN), Deep Learning (DL)
Divisions
PHYSICS
Funders
Ministry of Higher Education (MOHE) Malaysia, under Long-Term Research Grant Scheme (LRGS/1/2020/UM/01/5/1)
Publication Title
Computational Materials Science
Volume
241
Publisher
Elsevier
Publisher Location
RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS