Genetic apporach to targeted evolution of pinning landscapes in type-II supercondctors using large-scale simulations

The critical current in type-II superconductors largely depends on the pinning effectiveness of magnetic vortices. In practice, pinning defects can be grown chemically, e.g., self-assembled nanoparticles and nanorods, or introduced artificially by, e.g., ion irradiation. Here, we present a novel numerical technique which make it possible to determine the pinning landscape having the maximum possible critical current among all arbitrary combinations of defect types. Our approach is based on a genetic algorithm, which can evolve the number of the defects as well as position, size, and shape of each individual defect, in combination with the time-dependent Ginzburg-Landau equation, which is used to calculate the critical current for a given configuration. As an application, we determined the best possible configuration of metallic inclusions in a sample in fixed magnetic field perpendicular to the current.