Accelerating Materials Design via LLM-Guided Evolutionary Search

• URL: http://arxiv.org/abs/2510.22503v1
• Date: Sun, 26 Oct 2025 02:47:15 GMT
• Title: Accelerating Materials Design via LLM-Guided Evolutionary Search
• Abstract summary: We present LLM-guided Evolution for MAterials design (LLEMA)<n>It couples scientific knowledge embedded in large language models with chemistry-informed evolutionary rules and memory-based refinement.<n> evaluated on 14 realistic tasks spanning electronics, energy, coatings, optics, and aerospace.
• Score: 12.872214023537898
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Materials discovery requires navigating vast chemical and structural spaces while satisfying multiple, often conflicting, objectives. We present LLM-guided Evolution for MAterials 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 14 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. Code: https://github.com/scientific-discovery/LLEMA

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