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Studying critical currents, critical temperatures, and critical fields carries substantial importance in the field of superconductivity. In this work, we study critical currents in the current–voltage characteristics of a diluted-square lattice on an Nb film. Our measurements are based on a commercially available Physical Properties Measurement System, which may prove time consuming and costly for repeated measurements for a wide range of parameters. We therefore propose a technique based on artificial neural networks to facilitate extrapolation of these curves for unforeseen values of temperature and magnetic fields. We demonstrate that our proposed algorithm predicts the curves with an immaculate precision and minimal overhead, which may as well be adopted for prediction in other types of regular and diluted lattices. In addition, we present a detailed comparison between three artificial neural networks architectures with respect to their prediction efficiency, computation time, and number of iterations to converge to an optimal solution

Prediction of Critical Currents for a Diluted Square Lattice Using Artificial Neural Networks