The 2025 MSE Research Data Forum by NFDI-MatWerk
The forum focused on current research in materials science, its data challenges, and community networking. The first edition took place from 8-10 July 2025, in Siegburg and brought together 80 participants from the materials science and engineering community as well as the NFDI-MatWerk consortium. Over three days, presentations, discussions, and hands-on demonstrations addressed both current community research topics and their broad range of data scenarios, as well as solutions developed by NFDI-MatWerk. The scientific presentations were technically in-depth rather than broad. The smaller scale of the event also fostered more intensive exchanges. These two aspects received particularly positive feedback from materials science experts. Relevant topics included digital transformation in laboratories, handling experimental and simulation data, everyday workflows for researchers, and supporting AI and machine learning. Demonstrator sessions and posters added further format opportunities for practical insights and networking, as well as for planned developments. NFDI-MatWerk has established a recurring platform. In even-numbered years, the consortium contributes to the MSE Congress, the largest community conference. In odd years, the MSE Research Data Forum focuses specifically on research data practices. Together, they provide the community with a reliable space for exchange while, at the same time, offering NFDI-MatWerk valuable input for the further development of its services and solutions.
NFDI-MatWerk Summer/Winter School
The events provide participants with practical insights and tools to manage the research data under consideration of FAIR principles. The central challenge within the MatWerk community is the digital representation of materials and their relevant process and load parameters. NFDI-MatWerk provides tailored information and solutions for dealing with research data in MSE. Already 5 Seasonal Schools have been launched, with the first one done in September 2023 at Saarland University. The workshops featured interactive sessions that explored various aspects of research data management, such as metadata, FAIR-principles and electronic lab books. Participants engaged in hands-on exercises designed to apply theoretical concepts in real-world scenarios. With the time, also new developed solutions created within the NFDI project, like for instance Coscine, PyIron or Chaltene have been included in the program. At the end of the Schools evaluations were made, in which most participants expressed a new found confidence in their ability to approach challenges with skills learned. The events were very successful, achieving their goal of unlocking FAIR mindset among attendees. To build on this success the NFDI-MatWerk will continue the further development and delivery of such schools, that has proof to be excellent tools on one sensibilize the necessity of RDM and on the other side for bringing new developed solutions to the MSE-research community.
Pyiron
The implementation of pyiron-based automated workflows fundamentally improves the creation of machine learning interatomic potentials (MLPs) in materials science. This comprehensive and user-friendly framework, built upon the pyiron integrated development environment (IDE), allows researchers to seamlessly navigate the entire MLP development cycle. The process begins with the creation of systematic Density Functional Theory (DFT) databases, followed by fitting the DFT data to various empirical potentials or MLPs, and culminates in the validation of these potentials through a largely automated approach. The framework's power is exemplified through its application to three distinct classes of interatomic potentials: the embedded atom method (EAM), high-dimensional neural network potentials (HDNNP), and atomic cluster expansion (ACE). Each method showcases the versatility and efficiency of the pyiron workflows. A notable success story involves the computation of a binary composition-temperature phase diagram for the Al-Li alloy, a lightweight material critical to the aerospace industry. This advanced validation not only demonstrates the framework's capabilities but also highlights its potential to accelerate research and development in materials engineering, paving the way for innovative applications in various technological fields. A more in-depth description of successful pyiron implementation can be found in the Nature Communications article.
atomRDF
atomRDF is a tool designed for material scientists that enables the creation, manipulation, and querying of atomic structures based on ontologies. Powered by Pyscal3, atomRDF allows for the easy creation of both bulk and defect structures, which are automatically annotated. This makes the structural database easy to query and convert to other file formats. Furthermore, atomRDF improves not only data quality and traceability, but also the integration of data into automated workflows and AI-driven analyses. By supporting the FAIR principles (findable, accessible, interoperable, and reusable), atomRDF helps researchers to make their data more usable, shareable, and impactful in the context of modern data-centric science.
The NFDI-MatWerk Ontology and Knowledge Graph
One of the key successes of NFDI-MatWerk is the development of the MatWerk Ontology (MWO) and the Materials Science and Engineering Knowledge Graph (MSE-KG). Together, these resources transform the way research data in materials science is described, linked, and reused. The MWO provides a consistent semantic framework that captures the entire research lifecycle, including materials, processes, experiments, properties, and tools, thereby ensuring interoperability across projects and institutions. The MSE-KG is built on this foundation and connects datasets, publications, software, workflows, people, and organizations on a single interactive platform. This allows users to search and analyze data semantically, trace results back to their origins, and integrate workflows for reproducible science. By aligning with international standards and the FAIR principles, these components replace isolated data silos with a sustainable knowledge infrastructure. The result is a digital ecosystem that strengthens collaboration, accelerates research, and opens new pathways for innovation in materials science and engineering. This transition from isolated data silos to a shared semantic infrastructure accelerates research, enhances collaboration, and fosters innovation in materials science and engineering. Zenodo Repository
Increase of number of submissions to the MSE conference
The MSE conference is the premier scientific event in the field of materials science and engineering (MSE) in Germany. It serves as a central platform where researchers, industry representatives, and policymakers meet to present new findings, exchange ideas, and discuss the field's future directions. Over the years, the conference has expanded its scope to include emerging scientific and technological developments. One notable trend is the increasing focus on research data management within the MSE community. Since the inception of the NFDI-MatWerk project, the number of conference contributions on this subject has grown significantly, rising from five submissions in 2020 to 36 in 2022 and reaching 106 in 2024. This trend illustrates a significant qualitative shift in how research data is perceived within the field, as well as a substantial quantitative growth. The increase in submissions underscores the growing need for discussions and knowledge exchanges about how to systematically collect, share, and reuse data to advance materials research. This reflects the impact of NFDI-MatWerk, which promotes data infrastructures and FAIR data principles, as well as the influence of initiatives like MaterialDigital. At the same time, this trend mirrors a broader shift within the materials science community toward digitalization and data-driven research approaches. Together, these developments demonstrate that research data management has become an integral and growing topic at the MSE conference.
BAM Data Store Days 2025
Data Store Days at the Bundesanstalt für Materialforschung und -prüfung (BAM) brought together researchers and developers to improve FAIR data practices with openBIS. The event featured success stories, basic introductions to openBIS, and collaborative sessions to launch tools and strategies for better research data management in materials science.