MatScIE: An automated tool for the generation of databases of methods and parameters used in the computational materials science literature
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Abstract (arXiv)
The number of published articles in the field of materials science is growing rapidly every year. This comparatively unstructured data source, which contains a large amount of information, has a restriction on its re-usability, as the information needed to carry out further calculations using the data in it must be extracted manually. It is very important to obtain valid and contextually correct information from the online (offline) data, as it can be useful not only to generate inputs for further calculations, but also to incorporate them into a querying framework. Retaining this context as a priority, we have developed an automated tool, MatScIE (Material Scince Information Extractor) that can extract relevant information from material science literature and make a structured database that is much easier to use for material simulations. Specifically, we extract the material details, methods, code, parameters, and structure from the various research articles. Finally, we created a web application where users can upload published articles and view/download the information obtained from this tool and can create their own databases for their personal uses.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Sep 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 5; 2025: 4.
10 papers.
- 2021MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extractioncited by 82
- 2024Materials science in the era of large language models: a perspectivecited by 23
- 2025LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generationcited by 12
- 2024MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Constructioncited by 11
- 2024Intent Detection and Entity Extraction from BioMedical Literaturecited by 6
- 2025Text Takes Over: A Study of Modality Bias in Multimodal Intent Detectioncited by 6
- 2024On The Persona-based Summarization of Domain-Specific Documentscited by 4
- 2024A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intentscited by 3
- 2025Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documentscited by 1
- 2025IDALC: A Semi-Supervised Framework for Intent Detection and Active Learning based Correctioncited by 0
- 2025IDALC: A Semi-Supervised Framework for Intent Detection and Active Learning based Correction8 Nov 2025
- 2025LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generation27 Oct 2025
- 2025Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents13 Sep 2025
- 2025Text Takes Over: A Study of Modality Bias in Multimodal Intent Detection22 Aug 2025
- 2024A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents29 Oct 2024
- 2024On The Persona-based Summarization of Domain-Specific Documents6 Jun 2024
- 2024Intent Detection and Entity Extraction from BioMedical Literature4 Apr 2024
- 2024Materials science in the era of large language models: a perspective11 Mar 2024
- 2024MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction18 Jan 2024
- 2021MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction30 Sep 2021