DigiMOF:通过文本挖掘生成的金属有机框架合成信息的数据库
Lawson T Glasby1, Kristian Gubsch1, Rosalee Bence1
1Department of Chemical and Biological Engineering, The University of Sheffield, Sheffield S1 3JD, U.K.
Chemistry of materials : a publication of the American Chemical Society
|June 19, 2023
概括
研究人员通过挖掘MOF出版物来创建DigiMOF数据库,以获得合成数据. 这种开源资源加速了各种应用的新型金属有机框架 (MOF) 的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 化学信息学 化学信息学
背景情况:
- 材料空间的广,特别是金属有机框架 (MOF),对高效的材料识别提出了挑战.
- 高通量计算方法和机器学习有助于MOF选,但经常忽视合成细节.
- 从已发表的文献中提取材料信息学知识对于提高MOF发现效率至关重要.
研究的目的:
- 开发一个开源数据库,DigiMOF,专注于MOFs的合成特性.
- 为了利用化学意识的自然语言处理从MOF文献中自动提取数据.
- 创建MOF合成数据的集中存储库,用于增强材料发现.
主要方法:
- 调整了ChemDataExtractor (CDE) 工具,用于化学意识的自然语言处理和网络抓取.
- 使用剑桥结构数据库 (CSD) MOF子集下载和处理43,281篇MOF期刊文章.
- 提取了超过52,680种属性,包括合成方法,溶剂,链接剂,金属前体和15501种独特的MOF的拓学.
- 开发了一种方法,通过转换化学名称并将其与商业链接清单相匹配来识别链接类型.
主要成果:
- 创建了DigiMOF数据库,这是MOF合成性质的开源存储库.
- 成功提取和结构化了合成方法,溶剂,链接剂,金属前体和拓学的数据.
- 确定了链接类型,并分析了化学品的相关成本.
- 包括额外的数据,如可访问的表面积,孔隙尺寸和3DMOF的密度.
结论:
- DigiMOF数据库集中了出版物中嵌入的关键合成MOF数据.
- 该资源有助于快速搜索具有特定性质的MOF,并分析生产途径.
- 公共可用的数据库和软件使研究人员能够加速MOF的发现,并探索材料开发的新途径.
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