使用文本挖掘从科学文献中提取化学信息:为固态电解质构建离子导电数据库
1School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea.
ACS omega
|May 30, 2023
概括
研究人员开发了一种文本挖掘方法,自动从科学论文中提取固态电解质离子导电率. 这加快了为更安全的电动汽车电池创建关键材料数据库的速度.
科学领域:
- 材料科学 材料科学 材料科学
- 电化学 电化学 电化学
- 计算化学计算化学
背景情况:
- 对电动汽车 (EV) 的需求不断增长,增加了对传统液体电解质的安全担忧.
- 可充电电池中的液体电解质存在着火灾和爆炸的风险.
- 固态电解质 (SSEs) 提供了更大的稳定性,推动了对高离子导电性的材料的研究.
研究的目的:
- 从科学文献中自动提取SSE的离子导电数据.
- 构建一个全面的材料数据库,以加速SSE发现.
- 为了克服手动数据收集的耗时和重复性.
主要方法:
- 开发了一个文本挖掘管道,包括文档处理,自然语言预处理,阶段解析和关系提取.
- 应用该模型从38项研究中提取离子导电率以验证性能.
- 成功区分离子电导率和电导率,将不区分的记录从93%降至24.3%.
主要成果:
- 通过将提取的导电率与实际值进行比较,验证了文本挖掘模型的准确性.
- 在电池研究数据中显著提高了区分离子导电性和电导电性的能力.
- 从3258篇论文中构建了一个大规模的离子导电数据库.
结论:
- 自动化文本挖掘是快速构建SSE材料数据库的有效方法.
- 开发的数据库加速了用于更安全的电动汽车电池的新的,稳定的SSEs的探索和发现.
- 这种方法提高了数据可访问性和固态电池开发的研究效率.
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