Synergizing ontologies and graph databases for highly flexible materials-to-device workflow representations

The escalating adoption of high-throughput methods in applied materials science dramatically increases the amount of generated data, demandingthe deployment and use of sophisticated data-driven methods. To exploit the full potential of these accelerated approaches, the generated data needs to be managed, preserved, and shared. Its heterogeneity calls for highly flexible data models to represent data from fabrication workflows, measurements, and simulations. We propose employing a native graph database to store the data instead of relying on rigid relational data models. To develop a flexible and extendable data model, we have created an EMMO-based ontology, where EMMO stands for European Materials Modelling Ontology. The Python framework Django is used to allow for the intuitive integration into the virtual materials intelligence platform, VIMI. The Django framework relies on the Object-Graph-Mapper (OGM) neomodel to create a mapping between database classes and python objects. The model can store the whole bandwidth of data, from fabrication to simulation data. Implementing the database into a platform will encourage researchers to share data while profiting from rich and highly curated data to accelerate their research.