A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing.

ABSTRACT: The ever-increasing number of materials science articles makes it hard to infer chemistry-structure-property relations from literature. We used natural language processing methods to automatically extract material property data from the abstracts of polymer literature. As a component of our pipeline, we trained MaterialsBERT, a language model, using 2.4 million materials science abstracts, which outperforms other baseline models in three out of five named entity recognition datasets. Using this pipeline, we obtained ~300,000 material property records from ~130,000 abstracts in 60 hours. The extracted data was analyzed for a diverse range of applications such as fuel cells, supercapacitors, and polymer solar cells to recover non-trivial insights. The data extracted through our pipeline is m

SUBMITTER: Shetty P 

PROVIDER: S-EPMC10073792 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature