MOF研究中的文本挖掘:从手动策划到基于大型语言模型的自动化
Suyeon Bae1, Mingyu Jeon2, Hoi Ri Moon1
1Department of Chemistry and Nanoscience, Ewha Womans University, Seoul, 03760, Republic of Korea. hoirimoon@ewha.ac.kr.
Chemical communications (Cambridge, England)
|July 4, 2025
概括
文本挖掘通过从文献中提取数据来推进金属有机框架 (MOF) 研究. 大型语言模型 (LLM) 增强了这一过程,使准确的预测和未来的AI集成成为可能.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 数据科学数据科学数据科学
背景情况:
- 庞大且快速增长的金属有机框架 (MOF) 文献对高效的知识提取提出了挑战.
- 文本挖掘通过将非结构化科学文本转化为结构化,可用于MOF研究的可用数据提供了一个解决方案.
研究的目的:
- 审查应用到MOF研究的文本挖掘技术的演变.
- 分析自然语言处理 (NLP),机器学习 (ML) 和大型语言模型 (LLM) 对MOF数据提取的影响.
- 探索文本挖掘在MOF领域的应用和未来潜力.
主要方法:
- 审查历史文本挖掘方法 (手动策划,基于规则的方法).
- 分析基础NLP和ML技术 (命名实体识别,矢量嵌入).
- 深入研究基于LLM的信息提取框架及其准确性.
主要成果:
- 基于LLM的自动化代表了MOF文本挖掘的重大突破.
- 文本挖掘方法,特别是LLM,在预测MOF合成性,特性和稳定性方面表现出准确性.
- 在MOF研究中识别和比较了文本挖掘的各种应用.
结论:
- 文本挖掘,特别是LLM驱动的方法,对于加速数据驱动的MOF研究至关重要.
- 未来的方向包括将文本挖掘集成到交互式UI,自主实验室和多模式AI系统中.
- 这一审查为研究人员在MOF科学中采用和推进文本挖掘提供了基础.
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