Toward Full Interoperability in Materials Science: Integrating Workflows With Knowledge Graphs

Interoperability is a central challenge in modern materials science as scientific workflows increasingly need to integrate heterogeneous computational methods, experimental infrastructures, data platforms, and AI‐assisted systems. This perspective presents interoperability concepts from European digitalization initiatives in materials science namely, Platform MaterialDigital, NFDI‐MatWerk, and MaterialsCommons, which focus on workflow management systems (WfMSs), semantic representations, and knowledge graph technologies, while relating these developments to the broader landscape of materials informatics. Since various WfMSs address different requirements, interoperable abstraction layers such as the Python Workflow Definition are needed and enable workflows to be exchanged between systems while preserving reproducibility. Beyond technical interoperability, the review highlights the growing importance of semantic interoperability through ontology‐driven frameworks, including atomRDF, the Abstract Workflow Language, and semantikon. These approaches enrich workflows with machine‐readable semantic annotations, provenance information, and Resource Description Framework‐based knowledge graphs, enabling workflows to become semantically transparent and reusable scientific objects. The growing importance of semantically annotated workflows for the integration of computational and experimental workflows within Materials Acceleration Platforms and the emerging role of large language models and AI‐assisted workflow orchestration are also emphasized. Together, these developments outline a multilayered interoperability ecosystem that combines workflow execution, semantic reasoning, provenance tracking, and AI‐driven scientific automation within unified materials research infrastructures.