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Material science literature is a rich source of factual information aboutvarious categories of entities (like materials and compositions) and variousrelations between these entities, such as conductivity, voltage, etc.Automatically extracting this information to generate a material scienceknowledge base is a challenging task. In this paper, we propose MatSciRE(Material Science Relation Extractor), a Pointer Network-based encoder-decoderframework, to jointly extract entities and relations from material sciencearticles as a triplet ($entity1, relation, entity2$). Specifically, we targetthe battery materials and identify five relations to work on - conductivity,coulombic efficiency, capacity, voltage, and energy. Our proposed approachachieved a much better F1-score (0.771) than a previous attempt usingChemDataExtractor (0.716). The overall graphical framework of MatSciRE is shownin Fig 1. The material information is extracted from material scienceliterature in the form of entity-relation triplets using MatSciRE.

MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction