Common Strategies for Advancing the Digital Transformation in Materials Science
- Materials science and engineering is inherently interdisciplinary, bridging the natural sciences and engineering domains. This diversity is reflected in its digitalisation efforts, where subdomains often define distinct data structures, taxonomies, and terminologies. As a result, semantic ambiguities and fragmented research data management remain major obstacles to data integration across the field.
Recent advances in AI underscore the role of data as a strategic resource for innovation. However, the effectiveness of these methods depends on the availability, completeness, and quality of data. Well-structured and semantically annotated materials and process data that is processed and stored using semantic technologies, for example, can enable breakthroughs, particularly at the interface of processing, structure, properties, and performance. A systematic, coordinated approach grounded in the FAIR guiding principles and lifecycle-oriented data management is therefore essential.
TheMaterials science and engineering is inherently interdisciplinary, bridging the natural sciences and engineering domains. This diversity is reflected in its digitalisation efforts, where subdomains often define distinct data structures, taxonomies, and terminologies. As a result, semantic ambiguities and fragmented research data management remain major obstacles to data integration across the field.
Recent advances in AI underscore the role of data as a strategic resource for innovation. However, the effectiveness of these methods depends on the availability, completeness, and quality of data. Well-structured and semantically annotated materials and process data that is processed and stored using semantic technologies, for example, can enable breakthroughs, particularly at the interface of processing, structure, properties, and performance. A systematic, coordinated approach grounded in the FAIR guiding principles and lifecycle-oriented data management is therefore essential.
The German MaterialDigital Initiative, funded by the Federal Ministry of Education and Research, exemplifies such a collaborative framework. It brings together diverse project consortia, fostering community-driven consensus through exchange forums like the Ontology Playground, and supporting harmonized solutions for semantic interoperability.
By strengthening data culture and semantic infrastructure, collaborative digitalisation initiatives offer a scalable path to accelerated materials development and reproducible scientific progress paving the way for more efficient, data-driven innovation in materials science and engineering.…

