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An overwhelmingly large amount of knowledge in the materials domain isgenerated and stored as text published in peer-reviewed scientific literature.Recent developments in natural language processing, such as bidirectionalencoder representations from transformers (BERT) models, provide promisingtools to extract information from these texts. However, direct application ofthese models in the materials domain may yield suboptimal results as the modelsthemselves may not be trained on notations and jargon that are specific to thedomain. Here, we present a materials-aware language model, namely, MatSciBERT,which is trained on a large corpus of scientific literature published in thematerials domain. We further evaluate the performance of MatSciBERT on threedownstream tasks, namely, abstract classification, named entity recognition,and relation extraction, on different materials datasets. We show thatMatSciBERT outperforms SciBERT, a language model trained on science corpus, onall the tasks. Further, we discuss some of the applications of MatSciBERT inthe materials domain for extracting information, which can, in turn, contributeto materials discovery or optimization. Finally, to make the work accessible tothe larger materials community, we make the pretrained and finetuned weightsand the models of MatSciBERT freely accessible.

MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction