Multilayer neural network models for critical temperature of cuprate superconductors

Abstract

A multi-layer neural network is used to extract the value of a superconductor’s Tc of cuprate from two models. The first model extracts Tc from the structure’s chemical composition whereas the second model extracts Tc from the structure’s chemical composition and the lattice parameters. The back-propagation algorithm is used to find the empirical equation of Tc. It can calculate error signals and redistribute backward propagation signals. This paper studies four systems of cuprate superconductors: YBaCuO, BiSrCaCuO, TlBaCaCuO, and HgBaCaCuO. In the first model, the Tc of four high-temperature oxide superconductors are calculated as a function of eight parameters and one output, which is Tc. 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 Tc of cuprate superconductors. Using the obtained Tc prediction model, the scope is expanded to include the eleven unexplored multi-element materials. Candidates for superconductors with a higher Tc that can be synthesized are proposed. By comparison with other models of machine learning, the suggested models in this paper give the highest Tc for predicting new cuprate superconductors.