Automated knowledge extraction from polymer literature using natural language processing

Materials science literature has grown exponentially in recent years making it difficult for individuals to master all of this information. This constrains the formulation of new hypotheses that scientists can come up with. In this work, we explore whether materials science knowledge can be automatically inferred from textual information contained in journal papers. Using a data set of 0.5 million polymer papers, we show, using natural language processing methods that vector representations trained for every word in our corpus can indeed capture this knowledge in a completely unsupervised manner. We perform time-based studies through which we track popularity of various polymers for different applications and predict new polymers for novel applications based solely on the domain knowledge contained in our data set. Using co-relations detected automatically from literature in this manner thus, opens up a new paradigm for materials discovery.

Automated summary

Materials science literature has grown exponentially, making it difficult for individuals to master all information; this study explores the automatic inference of materials science knowledge from textual information; using natural language processing methods, knowledge can be captured in an unsupervised manner and new applications predicted.