Discovering superior circuit topologies requires navigating an exponentially large design space-a challenge traditionally reserved for human experts. Existing AI methods either select from predefined templates or generate novel topologies at a limited scale without rigorous verification, leaving large-scale performance-driven discovery underexplored. We present PowerGenie, a framework for automated discovery of higher-performance reconfigurable power converters at scale. PowerGenie introduces: (1) an automated analytical framework that determines converter functionality and theoretical performance limits without component sizing or SPICE simulation, and (2) an evolutionary finetuning method that co-evolves a generative model with its training distribution through fitness selection and uniqueness verification. Unlike existing methods that suffer from mode collapse and overfitting, our approach achieves higher syntax validity, function validity, novelty rate, and figure-of-merit (FoM). PowerGenie discovers a novel 8-mode reconfigurable converter with 23% higher FoM than the best training topology. SPICE simulations confirm average absolute efficiency gains of 10% across 8 modes and up to 17% at a single mode. Code is available at https://github.com/xz-group/PowerGenie.

翻译：发现卓越的电路拓扑需要在一个指数级庞大的设计空间中进行探索——这一挑战传统上仅由人类专家承担。现有的人工智能方法要么从预定义的模板中选择，要么在有限规模下生成新颖拓扑而缺乏严格验证，使得大规模性能驱动的发现研究不足。我们提出了PowerGenie，一个用于大规模自动化发现高性能可重构功率变换器的框架。PowerGenie引入了：（1）一个自动化分析框架，无需进行元件参数设计或SPICE仿真即可确定变换器功能与理论性能极限；（2）一种进化式微调方法，通过适应度选择与唯一性验证，使生成模型与其训练分布协同进化。与现有方法易受模式崩溃和过拟合影响不同，我们的方法实现了更高的语法有效性、功能有效性、新颖率以及品质因数（FoM）。PowerGenie发现了一种新颖的8模式可重构变换器，其FoM比训练集中最佳拓扑高出23%。SPICE仿真证实其在8种模式下平均绝对效率提升达10%，在单一模式下最高提升达17%。代码发布于 https://github.com/xz-group/PowerGenie。