An automated materials and processes identification tool for material informatics using deep learning approach
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 k...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier Ltd
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/39186/ http://umpir.ump.edu.my/id/eprint/39186/1/An%20automated%20materials%20and%20processes%20identification%20tool%20for.pdf |
| _version_ | 1848825705494740992 |
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| author | Miah, M. Saef Ullah Junaida, Sulaiman Sarwar, Talha Nur, Ibrahim Masuduzzaman, Md Rajan, Jose |
| author_facet | Miah, M. Saef Ullah Junaida, Sulaiman Sarwar, Talha Nur, Ibrahim Masuduzzaman, Md Rajan, Jose |
| author_sort | Miah, M. Saef Ullah |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:33:10Z |
| format | Article |
| id | ump-39186 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:33:10Z |
| publishDate | 2023 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-391862023-11-06T00:47:27Z http://umpir.ump.edu.my/id/eprint/39186/ An automated materials and processes identification tool for material informatics using deep learning approach Miah, M. Saef Ullah Junaida, Sulaiman Sarwar, Talha Nur, Ibrahim Masuduzzaman, Md Rajan, Jose HD28 Management. Industrial Management QA75 Electronic computers. Computer science 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. Elsevier Ltd 2023 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/39186/1/An%20automated%20materials%20and%20processes%20identification%20tool%20for.pdf Miah, M. Saef Ullah and Junaida, Sulaiman and Sarwar, Talha and Nur, Ibrahim and Masuduzzaman, Md and Rajan, Jose (2023) An automated materials and processes identification tool for material informatics using deep learning approach. Heliyon, 9 (e20003). ISSN 2405-8440. (Published) https://doi.org/10.1016/j.heliyon.2023.e20003 10.1016/j.heliyon.2023.e20003 |
| spellingShingle | HD28 Management. Industrial Management QA75 Electronic computers. Computer science Miah, M. Saef Ullah Junaida, Sulaiman Sarwar, Talha Nur, Ibrahim Masuduzzaman, Md Rajan, Jose An automated materials and processes identification tool for material informatics using deep learning approach |
| title | An automated materials and processes identification tool for material informatics using deep learning approach |
| title_full | An automated materials and processes identification tool for material informatics using deep learning approach |
| title_fullStr | An automated materials and processes identification tool for material informatics using deep learning approach |
| title_full_unstemmed | An automated materials and processes identification tool for material informatics using deep learning approach |
| title_short | An automated materials and processes identification tool for material informatics using deep learning approach |
| title_sort | automated materials and processes identification tool for material informatics using deep learning approach |
| topic | HD28 Management. Industrial Management QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/39186/ http://umpir.ump.edu.my/id/eprint/39186/ http://umpir.ump.edu.my/id/eprint/39186/ http://umpir.ump.edu.my/id/eprint/39186/1/An%20automated%20materials%20and%20processes%20identification%20tool%20for.pdf |