LLM-Guided Evolution under Search-Induced Distribution Shift for Organic Solar Cell Discovery

Organic solar cell (OSC) discovery requires searching an enormous chemical space using only sparse experimental supervision. Efficient discovery requires exploring beyond the limited set of experimentally observed molecules, while optimization progressively concentrates candidates in the sparsely sampled high-performance tail, where conventional predictors become less reliable. To address these challenges, we introduce a closed-loop molecular discovery framework that couples feedback-conditioned LLM evolutionary search with a discovery-oriented high-tail evaluator. The LLM guides semantic mutations over experimentally grounded molecules, enabling adaptive structural exploration beyond fixed recombination heuristics while preserving chemically meaningful scaffolds. For candidate evaluation, we propose Aggressive Tail Fusion, which combines a data-driven model with an experimentally calibrated physics model to improve reliability in the high-PCE regime. It reduces MAE on the experimentally observed top-5% subset from $2.472$ to $0.686$.