Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature

The number of published materials science articles has increased manyfold over the past few decades. Now, a major bottleneck in the materials discovery pipeline arises in connecting new results with the previously established literature. A potential solution to this problem is to map the unstructure...

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Bibliographic Details
Published in:	Journal of chemical information and modeling Vol. 59; no. 9; pp. 3692 - 3702
Main Authors:	Weston, L, Tshitoyan, V, Dagdelen, J, Kononova, O, Trewartha, A, Persson, K. A, Ceder, G, Jain, A
Format:	Journal Article
Language:	English
Published:	United States American Chemical Society 23-09-2019
Subjects:	Data mining embedding extraction Information retrieval Literature reviews Material properties materials biological databases Materials information MATERIALS SCIENCE Recognition Unstructured data Websites
Online Access:	Get full text
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Summary:	The number of published materials science articles has increased manyfold over the past few decades. Now, a major bottleneck in the materials discovery pipeline arises in connecting new results with the previously established literature. A potential solution to this problem is to map the unstructured raw text of published articles onto structured database entries that allow for programmatic querying. To this end, we apply text mining with named entity recognition (NER) for large-scale information extraction from the published materials science literature. The NER model is trained to extract summary-level information from materials science documents, including inorganic material mentions, sample descriptors, phase labels, material properties and applications, as well as any synthesis and characterization methods used. Our classifier achieves an accuracy (f 1) of 87%, and is applied to information extraction from 3.27 million materials science abstracts. We extract more than 80 million materials-science-related named entities, and the content of each abstract is represented as a database entry in a structured format. We demonstrate that simple database queries can be used to answer complex “meta-questions” of the published literature that would have previously required laborious, manual literature searches to answer. All of our data and functionality has been made freely available on our Github (https://github.com/materialsintelligence/matscholar) and website (http://matscholar.com), and we expect these results to accelerate the pace of future materials science discovery.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 AC02-05CH11231 USDOE Office of Science (SC)
ISSN:	1549-9596 1549-960X
DOI:	10.1021/acs.jcim.9b00470