fig9

From: AI agents for solid electrolytes: opportunities, challenges, and future directions

Figure 9. Ontology-guided framework for SE research integrating structured knowledge, AI reasoning, and autonomous experimentation. (A) Semantic layer linking physical assets, sensors, and experiments to a shared data/model space. Reproduced with permission from ref.^[167]. Copyright 2024, Wiley-VCH; (B) Multi-aspect ontology structure defining composition-structure-property-performance relations. Reproduced with permission from ref.^[170]. Copyright 2022, Wiley-VCH; (C) MatKG resource description framework (RDF) schema showing semantic connections among materials-related entities, properties, bibliographic attributes, relational nodes, and external knowledge sources. Reproduced with permission from ref.^[175]. Copyright 2024, Springer Nature; (D) AI agents using interoperable ontologies to drive closed-loop experimentation, hypothesis generation, and real-time knowledge updates. NER: Named entity recognition: DBpedia: a nucleus for a web of open data; SE: solid electrolyte; AI: artificial intelligence; MatKG: a knowledge graph in materials science.