标题： CrystalICL：实现晶体生成的上下文学习 显示英文标题

标题： CrystalICL: Enabling In-Context Learning for Crystal Generation

摘要： 设计具有所需物理化学性质的晶体材料仍然是材料科学中的一个基本挑战。 虽然大型语言模型（LLMs）已经展示了强大的上下文学习（ICL）能力，但现有的基于LLM的晶体生成方法仅限于零样本场景，并无法从少量样本场景中受益。 相反，人类专家通常通过修改相关已知结构来设计新材料，这与少量样本ICL范式紧密对齐。 受此启发，我们提出了CrystalICL，一种专为少量样本晶体生成设计的新模型。 具体而言，我们引入了一种基于空间群的晶体标记化方法，该方法有效降低了在LLMs中建模晶体对称性的复杂性。 我们进一步引入了一种条件-结构感知的混合指令微调框架和多任务指令微调策略，使模型能够通过从有限数据中捕捉结构-性能关系来更好地利用ICL。 在四个晶体生成基准上的大量实验表明，CrystalICL在条件和无条件生成任务上优于领先的基线方法。 显示英文摘要

摘要： Designing crystal materials with desired physicochemical properties remains a fundamental challenge in materials science. While large language models (LLMs) have demonstrated strong in-context learning (ICL) capabilities, existing LLM-based crystal generation approaches are limited to zero-shot scenarios and are unable to benefit from few-shot scenarios. In contrast, human experts typically design new materials by modifying relevant known structures which aligns closely with the few-shot ICL paradigm. Motivated by this, we propose CrystalICL, a novel model designed for few-shot crystal generation. Specifically, we introduce a space-group based crystal tokenization method, which effectively reduces the complexity of modeling crystal symmetry in LLMs. We further introduce a condition-structure aware hybrid instruction tuning framework and a multi-task instruction tuning strategy, enabling the model to better exploit ICL by capturing structure-property relationships from limited data. Extensive experiments on four crystal generation benchmarks demonstrate the superiority of CrystalICL over the leading baseline methods on conditional and unconditional generation tasks.