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

M Saef Ullah Miah, Junaida Sulaiman, Talha Bin Sarwar, Nur Ibrahim, Md Masuduzzaman, Rajan Jose

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In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

M Saef Ullah MiahFaculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Pekan, Pahang, 26600, Malaysia.

Junaida SulaimanFaculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Pekan, Pahang, 26600, Malaysia.

Talha Bin SarwarFaculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Pekan, Pahang, 26600, Malaysia.

Nur IbrahimFaculty of Electrical Engineering, Telkom University, Bandung, West Java, 40257, Indonesia.

Md MasuduzzamanKumoh National Institute of Technology, Gumi, 39076, Republic of Korea.

Rajan JoseFaculty of Industrial Sciences & Technology, Universiti Malaysia Pahang, 26300, Gambang, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article reports a tool that enables Materials Informatics, termed as MatRec, via a deep learning approach. The tool captures data, makes appropriate domain suggestions, extracts various entities such as materials and processes, and helps to establish entity-value relationships. This tool uses keyword extraction, a document similarity index to suggest relevant documents, and a deep learning approach employing Bi-LSTM for entity extraction. For example, materials and processes for electrical charge storage under an electric double layer capacitor (EDLC) mechanism are demonstrated herewith. A knowledge graph approach finds and visualizes different latent knowledge sets from the processed information. The MatRec received an F1 score of 9̃6% for entity extraction, 8̃3% for material-value relationship extraction, and 8̃7% for process-value relationship extraction, respectively. The proposed MatRec could be extended to solve material selection issues for various applications and could be an excellent tool for academia and industry.

Indexed as

EDLCEntity-value extractionKnowledge graphMaterial informaticsMaterials 4.0Materials discoveryProcess discovery

Identifiers

PMID37809409

DOI10.1016/j.heliyon.2023.e20003

PMCPMC10559754

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.