  In the field of inorganic materials science, there is a growing demand to
extract knowledge such as physical properties and synthesis processes of
materials by machine-reading a large number of papers. This is because
materials researchers refer to many papers in order to come up with promising
terms of experiments for material synthesis. However, there are only a few
systems that can extract material names and their properties. This study
proposes a large-scale natural language processing (NLP) pipeline for
extracting material names and properties from materials science literature to
enable the search and retrieval of results in materials science. Therefore, we
propose a label definition for extracting material names and properties and
accordingly build a corpus containing 836 annotated paragraphs extracted from
301 papers for training a named entity recognition (NER) model. Experimental
results demonstrate the utility of this NER model; it achieves successful
extraction with a micro-F1 score of 78.1%. To demonstrate the efficacy of our
approach, we present a thorough evaluation on a real-world automatically
annotated corpus by applying our trained NER model to 12,895 materials science
papers. We analyze the trend in materials science by visualizing the outputs of
the NLP pipeline. For example, the country-by-year analysis indicates that in
recent years, the number of papers on "MoS2," a material used in perovskite
solar cells, has been increasing rapidly in China but decreasing in the United
States. Further, according to the conditions-by-year analysis, the processing
temperature of the catalyst material "PEDOT:PSS" is shifting below 200 degree,
and the number of reports with a processing time exceeding 5 h is increasing
slightly.
