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指数级庞大的设计空间——这一挑战传统上仅由人类专家承担。现有AI方法要么从预定义模板中选择，要么在有限规模内生成新拓扑而缺乏严格验证，导致大规模性能驱动的拓扑发现研究不足。本文提出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包含两大创新：(1) 自动化解析框架，可在无需元件参数设计或SPICE仿真的前提下判定变换器功能与理论性能极限；(2) 进化微调方法，通过适应度选择与唯一性验证，使生成模型与其训练分布协同演化。相较于现有方法存在的模式崩溃与过拟合问题，本方法实现了更高的语法有效性、功能有效性、新颖率与品质因数(FoM)。PowerGenie发现了一种新型8模式可重构变换器，其FoM较训练集中最优拓扑提升23%。SPICE仿真验证其在8种模式下平均绝对效率提升10%，单模式下最高提升17%。代码将在论文发表时开源。