Fortunately, word embedding approaches have proven useful in capturing materials science knowledge available in published literature. Several studies have been conducted to retrieve the knowledge from pre-existing literature leveraging supervised natural language processing , but this needs manual labeling of the dataset before training. To overcome the lack of labeled data in the corpus of scientific literature, Tshitoyan et al. developed mat2vec, an unsupervised model to capture and encode materials science knowledge without manual labeling of the input data. This unsupervised word embedding approach called mat2vec is trained on a collection of abstracts mined from scientific literature. This does not require direct insertion of domain knowledge but it was shown that it could capture complex materials science concepts written into abstracts by subject matter experts.