Semantic Technology for Accelerated Battery Materials Research and Innovation

Materials research and innovation are pivotal to advancing battery technology and enabling the green transition. However, widely used trial-and-error approaches are too slow and under-utilize the valuable data that they generate. This presentation explores the pivotal role semantic technology - a fusion of artificial intelligence and web technologies - plays in accelerating research efforts towards next-generation battery materials. Semantic web technology like ontologies and knowledge graphs, helps researchers integrate heterogeneous data sources. This not only simplifies data retrieval by enabling precise, context-aware queries, but also augments the capabilities of machine learning. With semantically enriched data, researchers can craft models that are more adept at predicting material behaviours and identifying promising compositions.<br/><br/>This talk introduces the fundamentals of semantic technology and presents real case studies from battery materials research. This includes a specific focus on examples coming from both experiments and simulations. It is shown how semantic annotations can enable both human- and machine-readability of battery data to automate analysis and enable interoperability between different modelling frameworks. Harnessing the power of semantic technology in materials research not only expedites the development of next-generation battery materials but also fosters data-driven innovation and collaboration across diverse research domains.