Title: Accelerating Materials Design via LLM-Guided Evolutionary Search
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Large Language Models, Evolutionary Optimization, AI for Science, Material Discovery

Abstract: Materials discovery must navigate vast compositional spaces while satisfying multiple, often conflicting, objectives. We present **L**LM-guided **E**volution for **MA**terials design (LLEMA), a unified framework that couples the scientific knowledge embedded in large language models with chemistry-informed evolutionary rules and memory-based refinement. At each iteration, an LLM proposes crystallographically specified candidates under explicit property constraints; a surrogate-augmented oracle estimates physicochemical properties; and a multi-objective scorer updates success/failure memories to guide subsequent generations. Evaluated on **10** realistic tasks spanning electronics, energy, coatings, optics, and aerospace, LLEMA discovers candidates that are chemically plausible, thermodynamically stable, and property-aligned, achieving higher hit-rates and stronger Pareto fronts than generative and LLM-only baselines. Ablation studies confirm the importance of rule-guided generation, memory-based refinement, and surrogate prediction. By enforcing synthesizability and multi-objective trade-offs, LLEMA delivers a principled pathway to accelerate practical materials discovery.