用预训练大模型无须微调生成稳定晶体结构，效率更高。
MatLLMSearch: Crystal Structure Discovery with Evolution-Guided Large Language Models
- 用进化框架引导大模型进行隐式交叉与突变，保持化学合理性。
- 生成结构78.38%具亚稳态，31.7%经DFT验证稳定，优于专用模型。
- 无需微调即可用于结构预测与多目标优化，适合材料设计初学者。
晶体结构生成是材料科学的基础，有助于发现具有特定性能的新材料。现有方法依赖在材料数据库上对大语言模型（LLMs）进行大量微调，而我们证明预训练的LLMs可在不额外微调的情况下自动生成新颖且稳定的晶体结构。本框架将LLM作为智能提案代理，嵌入进化流程中，引导其执行隐式交叉与突变操作，同时确保化学有效性。实验表明，MatLLMSearch生成结构的机器学习势验证亚稳态率达78.38%，31.7%经密度泛函理论（DFT）验证稳定，优于CrystalTextLLM等专用模型。此外，该框架可适应多种材料设计任务，包括晶体结构预测与变形能、体模量等多目标优化，均无需微调。结果表明，该框架是一种通用高效的材料发现工具，实现了无需训练即可生成高质量新稳定结构，降低使用门槛与资源开销。
原文摘要 · Abstract (English)
Crystal structure generation is fundamental to materials science, enabling the discovery of novel materials with desired properties. While existing approaches leverage Large Language Models (LLMs) through extensive fine-tuning on materials databases, we show that pre-trained LLMs can inherently generate novel and stable crystal structures without additional fine-tuning. Our framework employs LLMs as intelligent proposal agents within an evolutionary pipeline that guides them to perform implicit crossover and mutation operations while maintaining chemical validity. We demonstrate that MatLLMSearch achieves a 78.38% metastable rate validated by machine learning interatomic potentials and 31.7% DFT-verified stability, outperforming specialized models such as CrystalTextLLM. Beyond crystal structure generation, we further demonstrate that our framework adapts to diverse materials design tasks, including crystal structure prediction and multi-objective optimization of properties such as deformation energy and bulk modulus, all without fine-tuning. These results establish our framework as a versatile and effective framework for consistent high-quality materials discovery, offering training-free generation of novel stable structures with reduced overhead and broader accessibility.
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