用12.6万对晶体结构与文本训练模型，让AI理解晶体知识。
Contrastive Learning of English Language and Crystal Graphs for Multimodal Representation of Materials Knowledge
- 通过对比学习融合晶体结构与英文描述，构建多模态表示
- 在零样本任务中超越最新大模型，达到顶尖性能
- 适合材料逆向设计、跨模态检索等研究者使用
人工智能正广泛应用于材料逆向设计，如晶体和分子。现有分子领域已将化学结构与文本知识结合以应对复杂指令，但晶体因数据稀缺（受研究偏向性影响）及文献缺乏语义标注，难以实现类似方法。本文提出一种对比语言-晶体模型（CLaC），基于新构建的12.6万组晶体结构-文本对进行预训练。为验证合成数据的有效性，我们还构建了从学术论文中提取的可比数据集。通过多种零样本跨模态任务和下游应用评估，结果表明，CLaC在理解晶体结构方面展现出卓越的零样本泛化能力，优于最新的大型语言模型。
原文摘要 · Abstract (English)
Artificial intelligence (AI) is increasingly used for the inverse design of materials, such as crystals and molecules. Existing AI research on molecules has integrated chemical structures of molecules with textual knowledge to adapt to complex instructions. However, this approach has been unattainable for crystals due to data scarcity from the biased distribution of investigated crystals and the lack of semantic supervision in peer-reviewed literature. In this work, we introduce a contrastive language-crystals model (CLaC) pre-trained on a newly synthesized dataset of 126k crystal structure-text pairs. To demonstrate the advantage of using synthetic data to overcome data scarcity, we constructed a comparable dataset extracted from academic papers. We evaluate CLaC's generalization ability through various zero-shot cross-modal tasks and downstream applications. In experiments, CLaC achieves state-of-the-art zero-shot generalization performance in understanding crystal structures, surpassing latest large language models.
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