DigiMOF: A Database of MOF Synthesis Information Generated via Text Mining
DigiMOF: A Database of MOF Synthesis Information Generated via Text Mining
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DigiMOF：通过文本挖掘生成的 MOF 合成信息数据库
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Peyman Z. Moghadam
中科院分区：
文献类型：
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作者：
Kristian Gubsch;Rosalee Bence;Lawson T. Glasby;Peyman Z. Moghadam
材料空间的巨大，特别是与金属有机框架（mof）有关的材料空间，产生了有效识别有前途的材料用于特定应用的关键问题。尽管包括机器学习在内的高通量计算方法在mof的快速筛选和合理设计中很有用，但它们往往忽略了与它们的合成相关的描述符。提高MOF发现效率的一种方法是对已发表的MOF论文进行数据挖掘，提取期刊文章中包含的材料信息学知识。在这里，通过采用化学感知自然语言处理工具ChemDataExtractor (CDE)，我们生成了一个专注于mof合成特性的开源数据库：DigiMOF数据库。使用CDE网络抓取包和剑桥结构数据库（CSD） MOF子集，我们自动下载了43,281篇独特的MOF期刊文章，提取了15,501种独特的MOF材料，并挖掘了52,680种相关性质，包括合成方法、溶剂、有机连接剂、金属前驱体和拓扑结构。这个集中的、结构化的数据库揭示了嵌入在数千份MOF出版物中的MOF合成数据。DigiMOF数据库
: The vastness of materials space, particularly that which is concerned with metal-organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data mine published MOF papers to extract the materials informatics knowledge contained within the journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials and text mined over 52,680 associated properties including synthesis method, solvent, organic linker, metal precursor, and topology. This centralised, structured database reveals the MOF synthetic data embedded within thousands of MOF publications. The DigiMOF database