The advent of natural language processing and large language models (LLMs)
has revolutionized the extraction of data from unstructured scholarly papers.
However, ensuring data trustworthiness remains a significant challenge. In this
paper, we introduce PropertyExtractor, an open-source tool that leverages
advanced conversational LLMs like Google Gemini-Pro and OpenAI GPT-4, blends
zero-shot with few-shot in-context learning, and employs engineered prompts for
the dynamic refinement of structured information hierarchies, enabling
autonomous, efficient, scalable, and accurate identification, extraction, and
verification of material property data. Our tests on material data demonstrate
precision and recall exceeding 93% with an error rate of approximately 10%,
highlighting the effectiveness and versatility of the toolkit. We apply
PropertyExtractor to generate a database of 2D material thicknesses, a critical
parameter for device integration. The rapid evolution of the field has outpaced
both experimental measurements and computational methods, creating a
significant data gap. Our work addresses this gap and showcases the potential
of PropertyExtractor as a reliable and efficient tool for the autonomous
generation of diverse material property databases, advancing the field.

利用自然语言处理和大型语言模型的研究进展，本论文介绍了 PropertyExtractor，这是一个开源工具，利用 Google Gemini-Pro 和 OpenAI GPT-4 等先进的对话式大型语言模型，将零样本学习与有限样本上下文学习相结合，并采用设计的提示来动态完善结构化信息层次结构，实现材料性能数据的自主、高效、可扩展和准确的识别、提取和验证。我们在材料数据上的测试表明，精确度和召回率均超过 93%，误差率约为 10%，突显了工具包的有效性和多功能性。我们应用 PropertyExtractor 生成了一个二维材料厚度数据库，这是装置集成的关键参数。该领域的快速发展已经超过了实验测量和计算方法，造成了重要的数据空白。我们的工作填补了这一空白，并展示了 PropertyExtractor 作为可靠高效的自动生成多样化材料属性数据库的潜力，推动了这一领域的发展。