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

• URL: http://arxiv.org/abs/2510.22424v1
• Date: Sat, 25 Oct 2025 20:01:33 GMT
• Title: Reinforcement learning-guided optimization of critical current in high-temperature superconductors
• Authors: Mouyang Cheng, Qiwei Wan, Bowen Yu, Eunbi Rha, Michael J Landry, Mingda Li,
• Abstract summary: High-temperature superconductors are essential for next-generation energy and quantum technologies.<n>But their performance is often limited by the critical current density ($J_c$), which is strongly influenced by microstructural defects.<n>Here we present an integrated workflow that combines reinforcement learning (RL) with time-dependent Ginzburg-Landau (TDGL) simulations.
• Score: 5.963780660451256
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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