Semantic integration of diverse data in materials science: Assessing Orowan strengthening
B. Bayerlein, M. Schilling, M. Curran, C. E. Campbell, A. A. Dima, H. Birkholz, J. W. Lau
Scientific Data, 2024
doi: 10.1038/s41597-024-03169-4
discovery-gemini-llm-reviewed-20260524
This study applies Semantic Web technologies to advance Materials Science and Engineering (MSE) through the integration of diverse datasets. Focusing on a 2000 series age-hardenable aluminum alloy, we correlate mechanical and microstructural properties derived
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from tensile tests and dark-field transmission electron microscopy across varied aging times. An expandable knowledge graph, constructed using the Tensile Test and Precipitate Geometry Ontologies aligned with the PMD Core Ontology, facilitates this integration. This approach adheres to FAIR principles and enables sophisticated analysis via SPARQL queries, revealing correlations consistent with the Orowan mechanism. The study highlights the potential of semantic data integration in MSE, offering a new approach for data-centric research and enhanced analytical capabilities.
Bernd Bayerlein; Markus Schilling; Philipp von Hartrott; Jörg Waitelonis; Semantic integration of diverse data in materials science: Assessing Orowan strengthening; Scientific Data; 2024; doi:10.1038/s41597-024-03169-4
Jupyter notebook demonstrating Orowan correlation using tensile test ontology (TTO) and precipitate geometry ontology (PGO). This example is provided by the PMD core team.
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