摘要：SummaryMaterials 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.Graphical AbstractDisplay OmittedHighlights•Word embeddings trained on a corpus of polymer papers•Common polymers and their corresponding applications analyzed over time•Polymer domain knowledge encoded in word vectors•Novel polymers for certain applications predicted and validated using word embeddingsComputer Science; Artificial Intelligence; Materials Science; Polymers
