Guiding New Battery Material Scale Up Through Automated Information and Relation Extraction

Improving the performance of novel electrode materials will enable competition with mature technologies like lithium-ion batteries (LIBs) at scale. To some extent novel electrode active materials can leverage the well-established manufacturing knowledge of lithium-ion batteries. However, several materials synthesis and performance challenges for electrode materials must be addressed for them to mature from lab to industrial scale. We employ natural language processing (NLP) tools to extract challenges from battery literature in the performance and synthesis of active battery materials. The tools also systematically review corresponding mitigation strategies. These selected mitigation strategies are then evaluated among a broad set of pre-existing lab-proposed mitigation strategies. These derived insights enable engineers in research and industry to navigate a large number of proposed strategies.