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一个自动化材料和过程识别工具,用于材料信息学,使用深度学习方法.

M Saef Ullah Miah1,2, Junaida Sulaiman1,3, Talha Bin Sarwar1

  • 1Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Pekan, Pahang, 26600, Malaysia.

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一个新的工具,MatRec,使用材料信息学的深度学习,提取材料和流程来帮助材料选择. 这种人工智能驱动的方法增强了学术界和工业界的数据分析.

关键词:
在EDLC中,我们可以通过EDLC.实体价值提取 实体价值提取知识图表知识图表材料信息学是一种材料信息学.材料4.0 材料4.0 材料4.0 材料发现材料的发现.过程发现过程发现.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学

背景情况:

  • 材料信息学对于加速材料的发现和选择至关重要.
  • 从非结构化文本中提取结构化信息是一个重大挑战.
  • 现有的方法可能缺乏处理复杂材料科学数据的复杂性.

研究的目的:

  • 介绍MatRec,一个新的材料信息学工具.
  • 利用深度学习进行自动数据提取和关系识别.
  • 证明该工具在识别用于存储电荷的材料和工艺方面的能力.

主要方法:

  • 使用深度学习方法,特别是Bi-LSTM,用于实体提取.
  • 实现了关键词提取和文档相似性,用于数据处理和建议.
  • 采用知识图的方法来可视化提取的信息和潜在的知识.

主要成果:

  • 在实体提取方面,F1得分为96%的高性能.
  • 在材料价值 (83%) 和工艺价值 (87%) 关系提取方面表现出强的结果.
  • 成功地应用了该工具来识别电双层电容器 (EDLC) 机制的材料和工艺.

结论:

  • MatRec有效地自动化了材料信息学中的数据捕获和关系提取.
  • 该工具显示出在各种应用中解决材料选择问题的巨大潜力.
  • 在学术和工业环境中,MatRec为研究人员提供了宝贵的资源.