从专利到数据集：提取氧化玻璃组成和性质
From Patents to Dataset: Scraping for Oxide Glass Compositions and Properties
AI总结：
本研究通过网络爬虫技术从专利中提取氧化玻璃的组成和性质数据，扩展了现有数据库的多样性与数量，为玻璃建模提供了更丰富的数据支持。
摘要：
在本工作中，我们提出了从专利表格中提取信息的网络爬虫技术，清理并结构化这些信息，以便未来用于预测机器学习模型中开发新玻璃。我们提取了与开发新玻璃相关的组成和三种属性，并将它们结构化为数据库，与其他可用数据集一起使用。我们还分析了所获得信息的一致性以及它对现有数据库的贡献。提取的液相线温度包含5,696种组成；第二子集包括4,298个折射率，最后是1,771种具有阿贝数的组成。此处的提取将可用信息增加了约10.4%（液相线温度）、6.6%（折射率）和4.9%（阿贝数）。影响超出了数量：新提取的数据引入了比现有数据库中更多样化的组成和属性值，从而扩展了玻璃建模应用中可访问的组成和属性空间。我们强调，新数据库中的组成包含相对更多的钛、镁、锆、铌、铁、锡和钇氧化物。
英文摘要：
In this work, we present web scraping techniques to extract in- formation from patent tables, clean and structure them for future use in predictive machine learning models to develop new glasses. We extracted compositions and three properties relevant to the development of new glasses and structured them into a database to be used together with information from other available datasets. We also analyzed the consistency of the information obtained and what it adds to the existing databases. The extracted liquidus temperatures comprise 5,696 compositions; the second subset includes 4,298 refractive indexes and, finally, 1,771 compositions with Abbe numbers. The extraction performed here increases the available information by approximately 10.4% for liquidus temperature, 6.6% for refractive index, and 4.9% for Abbe number. The impact extends beyond quantity: the newly extracted data introduce compositions with property values that are more diverse than those in existing databases, thereby expanding the accessible compositional and property space for glass modeling applications. We emphasize that the compositions of the new database contain relatively more titanium, magnesium, zirconium, niobium, iron, tin, and yttrium oxides than those of the existing bases.
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