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

• Main Authors: Miah, M. Saef Ullah, Junaida, Sulaiman, Sarwar, Talha, Nur, Ibrahim, Masuduzzaman, Md, Rajan, Jose
• Format: Article
• Language: English
• Published: Elsevier Ltd 2023
• Subjects: HD28 Management. Industrial Management; QA75 Electronic computers. Computer science
• Online Access: http://umpir.ump.edu.my/id/eprint/39186/1/An%20automated%20materials%20and%20processes%20identification%20tool%20for.pdf; http://umpir.ump.edu.my/id/eprint/39186/; https://doi.org/10.1016/j.heliyon.2023.e20003

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.