用强化学习优化高温超导体缺陷，使临界电流提升15倍。
Reinforcement learning-guided optimization of critical current in high-temperature superconductors
- 用强化学习自动搜索最优缺陷配置
- 临界电流达理论极限的60%，较随机初始化提升15倍
- 适合超导材料设计与高场器件应用
高温超导体在下一代能源与量子技术中至关重要，但其性能常受限于临界电流密度（J_c），而该参数受微观缺陷影响显著。由于缺陷类型、密度与空间关联性之间存在复杂相互作用，通过缺陷工程优化J_c极具挑战。本文提出一种整合强化学习（RL）与时间依赖吉尼茨-朗道（TDGL）模拟的集成工作流程，自主识别最大化J_c的最优缺陷构型。在该框架中，TDGL模拟生成电流-电压特性以评估J_c，作为奖励信号指导强化学习智能体迭代优化缺陷分布。研究发现，智能体在二维薄膜结构中发现了最优缺陷密度与相关性，显著增强涡旋钉扎能力，使J_c相较无缺陷薄膜提升约15倍，并接近理论解配对极限的60%。该强化学习驱动方法为缺陷工程提供了可扩展策略，对聚变磁体、粒子加速器等高场技术中的高温超导应用具有广泛意义。
原文摘要 · Abstract (English)
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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