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 will be released upon publication.

翻译：发现更优的电路拓扑结构需要遍历呈指数级增长的设计空间，这一挑战传统上依赖人类专家。现有AI方法或从预定义模板中选取拓扑，或在缺乏严格验证的条件下以有限规模生成新型拓扑，导致大规模性能驱动的拓扑发现仍未被充分探索。我们提出PowerGenie——一个用于可规模化自动发现高性能可重构电源转换器的框架。PowerGenie引入：（1）无需元件参数或SPICE仿真的自动化分析框架，可判定转换器功能并计算理论性能极限；（2）进化微调方法，通过适应度选择与唯一性验证协同进化生成模型及其训练分布。与现有方法易出现模式崩溃与过拟合不同，本方法在语法有效性、功能有效性、新颖率及品质因数（FoM）上均表现更优。PowerGenie发现了一种新型8模式可重构转换器，其FoM较最优训练拓扑提升23%。SPICE仿真验证其8种模式平均绝对效率增益达10%，单模式最高增益达17%。相关代码将在发表后开源。