  The past few years has seen the application of machine learning utilised in
the exploration of new materials. As in many fields of research - the vast
majority of knowledge is published as text, which poses challenges in either a
consolidated or statistical analysis across studies and reports. Such
challenges include the inability to extract quantitative information, and in
accessing the breadth of non-numerical information. To address this issue, the
application of natural language processing (NLP) has been explored in several
studies to date. In NLP, assignment of high-dimensional vectors, known as
embeddings, to passages of text preserves the syntactic and semantic
relationship between words. Embeddings rely on machine learning algorithms and
in the present work, we have employed the Word2Vec model, previously explored
by others, and the BERT model - applying them towards a unique challenge in
materials engineering. That challenge is the search for chromate replacements
in the field of corrosion protection. From a database of over 80 million
records, a down-selection of 5990 papers focused on the topic of corrosion
protection were examined using NLP. This study demonstrates it is possible to
extract knowledge from the automated interpretation of the scientific
literature and achieve expert human level insights.
