Automating Materials Exploration with a Semantic Knowledge Graph for Li-Ion Battery Cathodes

Abstract

The recent marriage of materials science and artificial intelligence has created the need to extract and collate materials information from the tremendous backlog of academic publications. However, this is notoriously hard to achieve in sophisticated application domains, such as Li-ion battery (LIB) cathodes, which require multiple variables for materials selection, making it challenging to automatically identify the critical terms in the text. Herein, a semantics representation framework, featuring a dual-attention module that refines word embeddings through multi-source information fusion, is proposed for literature mining of LIB cathodes. The word embeddings thus produced are biased toward domain-specific knowledge and can enable the detection of deep-seated associations among materials for targeted applications. Based on this framework, we establish a semantic knowledge graph dedicated to LIB cathodes, which allows us to unravel the latent materials relationships from scientific literature and even to discover candidate materials not yet exploited as cathodes before. This work provides a long-sought path to the realization of text-mining-based knowledge management for complicated materials systems with little dependence on domain expertise.