梳理化学大模型如何融合领域知识与多模态数据，推动科研加速。
From Generalist to Specialist: A Survey of Large Language Models for Chemistry
- 将化学专有知识与2D/3D结构、光谱等多模态信息融入大模型
- 提出化学大模型作为科研代理的框架，可调用化学工具自主探索
- 系统总结评估基准，指明未来研究方向，适合化学与AI交叉研究者
大语言模型（LLMs）已深刻改变日常生活并重塑自然语言处理范式。然而，主流LLMs基于网络文本预训练，在化学等前沿科学发现中仍显不足。化学领域数据稀缺，且2D图、3D结构、光谱等多模态数据复杂，带来独特挑战。尽管已有若干关于化学预训练语言模型（PLMs）的综述，但缺乏聚焦于面向化学的大语言模型的系统性调研。本文梳理了将领域知识与多模态信息融入LLMs的方法，将化学LLMs视为使用化学工具的智能体，并探讨其加速科学发现的潜力。同时，归纳现有评估基准以衡量化学能力。最后，批判性分析当前挑战，提出未来研究方向。本综述旨在帮助研究人员把握化学大模型前沿进展，激发该领域的创新应用。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have significantly transformed our daily life and established a new paradigm in natural language processing (NLP). However, the predominant pretraining of LLMs on extensive web-based texts remains insufficient for advanced scientific discovery, particularly in chemistry. The scarcity of specialized chemistry data, coupled with the complexity of multi-modal data such as 2D graph, 3D structure and spectrum, present distinct challenges. Although several studies have reviewed Pretrained Language Models (PLMs) in chemistry, there is a conspicuous absence of a systematic survey specifically focused on chemistry-oriented LLMs. In this paper, we outline methodologies for incorporating domain-specific chemistry knowledge and multi-modal information into LLMs, we also conceptualize chemistry LLMs as agents using chemistry tools and investigate their potential to accelerate scientific research. Additionally, we conclude the existing benchmarks to evaluate chemistry ability of LLMs. Finally, we critically examine the current challenges and identify promising directions for future research. Through this comprehensive survey, we aim to assist researchers in staying at the forefront of developments in chemistry LLMs and to inspire innovative applications in the field.
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