Reinforcement learning-guided optimization of critical current in high-temperature superconductors

TL;DR

This paper introduces a reinforcement learning framework combined with simulations to optimize defect configurations in high-temperature superconductors, significantly improving their critical current density for advanced technological applications.

Contribution

It presents a novel integrated workflow that autonomously discovers optimal defect patterns to enhance superconductor performance, bridging simulation and machine learning.

Findings

Achieved up to 60% of the theoretical depairing limit for $J_c$.

Up to 15-fold enhancement in critical current density over random defect configurations.

Discovered optimal defect densities and correlations for vortex pinning.

Abstract

High-temperature superconductors are essential for next-generation energy and quantum technologies, yet their performance is often limited by the critical current density ($J_c$), which is strongly influenced by microstructural defects. Optimizing $J_c$ through defect engineering is challenging due to the complex interplay of defect type, density, and spatial correlation. Here we present an integrated workflow that combines reinforcement learning (RL) with time-dependent Ginzburg-Landau (TDGL) simulations to autonomously identify optimal defect configurations that maximize $J_c$. In our framework, TDGL simulations generate current-voltage characteristics to evaluate $J_c$, which serves as the reward signal that guides the RL agent to iteratively refine defect configurations. We find that the agent discovers optimal defect densities and correlations in two-dimensional thin-film geometries, enhancing vortex pinning and $J_c$ relative to the pristine thin-film, approaching 60\% of theoretical depairing limit with up to 15-fold enhancement compared to random initialization. This RL-driven approach provides a scalable strategy for defect engineering, with broad implications for advancing HTS applications in fusion magnets, particle accelerators, and other high-field technologies.