  The number of scientific journal articles and reports being published about
energetic materials every year is growing exponentially, and therefore
extracting relevant information and actionable insights from the latest
research is becoming a considerable challenge. In this work we explore how
techniques from natural language processing and machine learning can be used to
automatically extract chemical insights from large collections of documents. We
first describe how to download and process documents from a variety of sources
- journal articles, conference proceedings (including NTREM), the US Patent &
Trademark Office, and the Defense Technical Information Center archive on
archive.org. We present a custom NLP pipeline which uses open source NLP tools
to identify the names of chemical compounds and relates them to function words
("underwater", "rocket", "pyrotechnic") and property words ("elastomer",
"non-toxic"). After explaining how word embeddings work we compare the utility
of two popular word embeddings - word2vec and GloVe. Chemical-chemical and
chemical-application relationships are obtained by doing computations with word
vectors. We show that word embeddings capture latent information about
energetic materials, so that related materials appear close together in the
word embedding space.
