CrystalICL: Enabling In-Context Learning for Crystal Generation

• URL: http://arxiv.org/abs/2508.20143v1
• Date: Wed, 27 Aug 2025 07:49:27 GMT
• Title: CrystalICL: Enabling In-Context Learning for Crystal Generation
• Authors: Ruobing Wang, Qiaoyu Tan, Yili Wang, Ying Wang, Xin Wang,
• Abstract summary: Large language models (LLMs) have demonstrated strong in-context learning (ICL) capabilities.<n>Existing LLM-based crystal generation approaches are limited to zero-shot scenarios and are unable to benefit from few-shot scenarios.<n>We propose CrystalICL, a novel model designed for few-shot crystal generation.
• Score: 12.641999605656409
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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