2024/11/19 by Vincent Nebel, Lisa Beran, Veit Königer +6 · 1 voice · 1 citation
Materials Science · Computer Science · #Machine Learning in Materials Science #Semantic Web and Ontologies #Service-Oriented Architecture and Web Services
paper · pdf · doi:10.1002/adem.202401540
openalex publication_date 2024/11/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Materials science research faces challenges due to diverse and evolving measurements, materials, and methods. Managing research data in a way that is understandable, comparable, and reproducible is essential for high data quality, particularly for data science and machine learning applications. In Li‐ion batteries research data storage concepts and structures vary widely between institutions and researchers, leading to difficulties in data comparison and understanding. To address the issue of data structuring, battery production and characterization ontology (BPCO) is developed. The ontology builds on existing ontologies like the Platform MaterialDigital core ontology and quantities, units, dimensions, and types ontology to model standard battery production processes, characterization methods, and materials. The BPCO is based on a workflow structure to be accessible to nonexperts and, unlike highly specialized existing ontologies, models the whole production process removing the need for separate data structures and enabling the identification of dependencies between parameters. This work builds upon a previously published paper in which the taxonomy and fundamental strategies for ontology development are established. The article presents the developed ontology and its use for structuring research data in three key use cases, that is, different experiments performed to validate the ontology's capabilities, provide feedback, and ensure its applicability.