  The ever-increasing number of materials science articles makes it hard to
infer chemistry-structure-property relations from published literature. We used
natural language processing (NLP) 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 when used as the encoder for
text. 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 made available through a web platform at https://polymerscholar.org which
can be used to locate material property data recorded in abstracts
conveniently. This work demonstrates the feasibility of an automatic pipeline
that starts from published literature and ends with a complete set of extracted
material property information.
