4.5 Article

MatScIE: An automated tool for the generation of databases of methods and parameters used in the computational materials science literature

Journal

COMPUTATIONAL MATERIALS SCIENCE
Volume 192, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.commatsci.2021.110325

Keywords

Sequence labeling; Information extraction; Material scientific articles

Ask authors/readers for more resources

The number of published articles in the field of materials science is increasing rapidly each year, but the information in these articles needs to be manually extracted for further calculations. To address this issue, researchers have developed an automated tool, MatScIE, which can extract relevant information from material science literature and create a structured database for easier use in material simulations.
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 reusability, 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 Science 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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available