# Targeted evolution of pinning landscapes for large superconducting critical currents

## TL;DR

This paper introduces a genetic algorithm approach to optimize defect landscapes in type-II superconductors, aiming to maximize critical currents by evolving pinning sites, with potential applications in high-field magnet design.

## Contribution

It presents a novel evolutionary method to design defect configurations in superconductors, improving critical current performance beyond traditional approaches.

## Key findings

- Genetic algorithm effectively optimizes pinning landscapes.
- Pre-existing defects can be enhanced through post-processing.
- Method adaptable to other material optimization problems.

## Abstract

The ability of type-II superconductors to carry large amounts of current at high magnetic fields is a key requirement for future design innovations in high-field magnets for accelerators and compact fusion reactors and largely depends on the vortex pinning landscape comprised of material defects. The complex interaction of vortices with defects that can be grown chemically, e.g., self-assembled nanoparticles and nanorods, or introduced by post-synthesis particle irradiation precludes a priori prediction of the critical current and can result in highly non-trivial effects on the critical current. Here, we borrow concepts from biological evolution to create a genetic algorithm evolving pinning landscapes to accommodate vortex pinning and determine the best possible configuration of inclusions for two different scenarios: an evolution process starting from a pristine system and one with pre-existing defects to demonstrate the potential for a post-processing approach to enhance critical currents. Furthermore, the presented approach is even more general and can be adapted to address various other targeted material optimization problems.