The microstructure of materials is characterized by crystallographic defects, which ultimately determine the material properties. Computational materials science uses methods and tools to predict and analyze the defect structure, where the increase of computational power has led to the generation of large amounts of data, increasing the need for the implementation of data-driven approaches. Specifically in the field of atomistic simulation methods, we currently face several challenges that impair data reusability: First, to facilitate the understanding and use of these computational samples (or atomic structures), well-described and harmonized metadata and data are crucial. However, most standardization approaches focus solely on perfect crystal structures, neglecting defects. Second, the fact that these calculations often involve a combination of different software tools and various file formats, results in interoperability issues and information loss. Finally, the reproducibility of the workflow used to set up a digital sample is frequently lacking.

To address these problems and allow re-use of the generated data, we have developed the Computational Materials Sample Ontology (CMSO) [1], an application-level ontology for material science computational samples. The use of the CMSO ontology is complemented by the development of domain-level ontologies describing crystallographic defects [2] and atomistic simulation concepts. CMSO initially focuses on describing structures at the atomistic level - as an example use case we will present the description of grain boundary data.

To aid domain scientists in implementing ontologies in their everyday research, we present a software tool for the automated annotation of structures using available atomic structure codes. Pyscal-rdf [3] provides a way for users to create knowledge graphs, improving the querying and findability of their research data. The combination of a controlled vocabulary and a complementary software tool for generating linked open data ensures interoperability between different file formats and software, while also offering the potential for data to be findable and reusable [4].

References

[1] https://purls.helmholtz-metadaten.de/cmso/

[2] https://github.com/OCDO/

[3] https://doi.org/10.5281/zenodo.8146527

[4] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al., Sci Data, 2016, 3, 160018.