# In silico optimization of critical currents in superconductors

## TL;DR

This paper compares various optimization strategies to identify the best defect structures in superconductors that maximize their critical current capacity, using simulations of different defect geometries.

## Contribution

It introduces a systematic comparison of local and global optimization methods for designing defect structures in superconductors to enhance critical currents.

## Key findings

- Global optimization outperforms local methods in finding optimal defect configurations.
- Optimal defect structures vary significantly with defect shape and placement.
- The methods successfully predict defect arrangements that could improve superconductor performance.

## Abstract

For many technological applications of superconductors the performance of a material is determined by the highest current it can carry losslessly - the critical current. In turn, the critical current can be controlled by adding non-superconducting defects in the superconductor matrix. Here we report on systematic comparison of different local and global optimization strategies to predict optimal structures of pinning centers leading to the highest possible critical currents. We demonstrate performance of these methods for a superconductor with randomly placed spherical, elliptical, and columnar defects.