让晶体生成模型学会少量样例快速学习，模仿专家设计思路。
CrystalICL: Enabling In-Context Learning for Crystal Generation
- 基于晶系分组的分词方法，降低对晶体对称性的建模复杂度。
- 在4个基准上超越现有方法，少样本条件下生成质量显著提升。
- 适合需要快速设计新晶体材料的研究者使用。
设计具有特定理化性质的晶体材料仍是材料科学中的核心挑战。尽管大语言模型（LLMs）展现出强大的上下文学习（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，无法利用少样本情形。相比之下，人类专家通常通过修改已知结构来设计新材料，这与少样本ICL范式高度契合。为此，我们提出CrystalICL，一种面向少样本晶体生成的新模型。具体而言，我们引入基于晶系的晶体分词方法，有效降低在LLM中建模晶体对称性的复杂度；同时提出条件-结构感知的混合指令微调框架和多任务指令微调策略，使模型能从有限数据中更好地捕捉结构-性能关系，从而更充分地利用ICL。在四个晶体生成基准上的大量实验表明，CrystalICL在条件与非条件生成任务中均优于领先基线方法。
原文摘要 · Abstract (English)
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.
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