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An automated materials and processes identification tool for material informatics using deep learning approach.

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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|October 9, 2023
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Summary
This summary is machine-generated.

A new tool, MatRec, uses deep learning for Materials Informatics, extracting materials and processes to aid material selection. This AI-driven approach enhances data analysis for academia and industry.

Keywords:
EDLCEntity-value extractionKnowledge graphMaterial informaticsMaterials 4.0Materials discoveryProcess discovery

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Area of Science:

  • Materials Science
  • Computer Science
  • Data Science

Background:

  • Materials Informatics is crucial for accelerating materials discovery and selection.
  • Extracting structured information from unstructured text is a significant challenge.
  • Existing methods may lack the sophistication to handle complex material science data.

Purpose of the Study:

  • To introduce MatRec, a novel tool for Materials Informatics.
  • To leverage deep learning for automated data extraction and relationship identification.
  • To demonstrate the tool's capability in identifying materials and processes for electrical charge storage.

Main Methods:

  • Utilized a deep learning approach, specifically Bi-LSTM, for entity extraction.
  • Implemented keyword extraction and document similarity for data processing and suggestions.
  • Employed a knowledge graph approach for visualizing extracted information and latent knowledge.

Main Results:

  • Achieved high performance with an F1 score of 96% for entity extraction.
  • Demonstrated strong results in material-value (83%) and process-value (87%) relationship extraction.
  • Successfully applied the tool to identify materials and processes for electric double layer capacitor (EDLC) mechanisms.

Conclusions:

  • MatRec effectively automates data capture and relationship extraction in Materials Informatics.
  • The tool shows significant potential for solving material selection problems across various applications.
  • MatRec offers a valuable resource for researchers in both academic and industrial settings.