用AI自动提取材料科学文献中的多模态数据，构建可检索的数据库。
Towards an automated workflow in materials science for combining multi-modal simulative and experimental information using data mining and large language models
- 通过NLP和视觉模型解析文献中的文本、图表、公式等信息
- 实现跨模拟与实验数据的快速检索与属性提取，加速研究进程
- 适合材料领域研究人员及需要知识融合的AI应用开发者
为在材料科学中检索与比较模拟与实验数据，需确保数据易于访问且机器可读。尽管开放科学推动了数据获取，但多数信息仍编码于科学文献中，限制了文献与材料属性的发现能力。本文展示了一种自动化工作流，利用自然语言处理与视觉变换器模型，将科学文献中的文本、图表、表格、公式及元数据转化为机器可读结构，构建可扩展的数据库。该数据库可融合本地未公开或私有材料数据，实现知识整合。研究表明，该流程显著提升信息检索速度、上下文定位精度及材料属性提取效率，以面心立方单晶微观结构分析为例。最终，基于检索增强生成（RAG）的大语言模型实现了高效问答聊天机器人。
原文摘要 · Abstract (English)
To retrieve and compare scientific data of simulations and experiments in materials science, data needs to be easily accessible and machine readable to qualify and quantify various materials science phenomena. The recent progress in open science leverages the accessibility to data. However, a majority of information is encoded within scientific documents limiting the capability of finding suitable literature as well as material properties. This manuscript showcases an automated workflow, which unravels the encoded information from scientific literature to a machine readable data structure of texts, figures, tables, equations and meta-data, using natural language processing and language as well as vision transformer models to generate a machine-readable database. The machine-readable database can be enriched with local data, as e.g. unpublished or private material data, leading to knowledge synthesis. The study shows that such an automated workflow accelerates information retrieval, proximate context detection and material property extraction from multi-modal input data exemplarily shown for the research field of microstructural analyses of face-centered cubic single crystals. Ultimately, a Retrieval-Augmented Generation (RAG) based Large Language Model (LLM) enables a fast and efficient question answering chat bot.
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