Representing experimental procedures in an unambiguous way that can be understood and reproduced by other scientists is at the heart of scientific progress. For centuries, these descriptions were made by humans and for humans, often assuming implicit or tacit knowledge. However, when Materials Acceleration Platforms (MAPs) and Self-Driving Labs (SDLs) are used for the autonomous discovery and optimization of materials, sharing knowledge, and workflows that were designed and executed by machines becomes increasingly important. These machines require an explicit, precise and accurate description and modeling of all process parameters and steps that need to be executed. To address these needs, especially in the domain of materials science and nano and advanced materials synthesis, we developed the Wet Chemical Synthesis Ontology (WCSO), which is based on the Platform MaterialDigital core ontology (PMDco) and the Basic Formal Ontology (BFO). The ontology contains recurring concepts from millions of wet chemical synthesis procedures in the scientific literature. We discuss the design considerations, concepts, and architecture of our ontology in detail, and demonstrate how it can be applied to the construction and querying of semantically annotated knowledge graphs from wet chemical nano- and advanced materials synthesis workflows that were previously designed for and then executed on an SDL. Using such formal representations and semantic annotations for describing synthesis procedures and workflows facilitates the reproducibility, sharing, and execution of synthesis procedures across different labs around the world that use different orchestrators for their robotic hardware.

Materials Science and Engineering (MSE) increasingly relies on data‐intensive, automated, and distributed workflows that span synthesis, manufacturing, characterization, design, and simulation. These settings require machine‐actionable representations of materials and processes that remain interoperable across laboratories, software stacks, and organizations. Therefore, Platform MaterialDigital Core Ontology (PMDco) 3.0 is introduced as a mid‐level ontology that provides a semantic framework for the processing–structure–properties paradigm in MSE. PMDco 3.0 adopts an architecture aligned with the Basic Formal Ontology that enables a logically consistent classification of fundamental MSE concepts and the explicit representation of intrinsic material properties, contextual roles and functions, and related information artifacts. The work outlines the technical curation approach that supports sustainable ontology evolution through reproducible builds, automated release generation, and systematic validation workflows. Representative semantic patterns are presented as reusable building blocks for consistent modeling and data mapping, including material object duality, intensive versus extensive qualities, role and function assignment, immaterial entities for spatial context, process modeling across production, assay, and computation, and the separation of requirements from observations via set points and measurements. PMDco 3.0 is intended to serve as a community‐driven anchor for interoperable domain and application ontologies and scalable semantic interoperability in MSE.

The MaterialDigital initiative represents a major driver toward the digitalization of material science. Next to providing a prototypical infrastructure required for building a shared data space and working on semantic interoperability of data, a core focus area of the Platform MaterialDigital (PMD) is the utilization of workflows to encapsulate data processing and simulation steps in accordance with findable, accessible, interoperable, and reusable principles. In collaboration with the funded projects of the initiative, the workflow working group strives to establish shared standards, enhancing the interoperability and reusability of scientific data processing steps. Central to this effort is the Workflow Store, a pivotal tool for disseminating workflows with the community, facilitating the exchange and replication of scientific methodologies. This article discusses the inherent challenges of adapting workflow concepts, providing the perspective on developing and using workflows in the respective domain of the various funded projects. Additionally, it introduces the Workflow Store's role within the initiative and outlines a future roadmap for the PMD workflow group, aiming to further refine and expand the role of scientific workflows as a means to advance digital transformation and foster collaborative research within material science.

This article describes advancements in the ongoing digital transformation in materials science and engineering. It is driven by domain-specific successes and the development of specialized digital data spaces. There is an evident and increasing need for standardization across various subdomains to support science data exchange across entities. The MaterialDigital Initiative, funded by the German Federal Ministry of Education and Research, takes on a key role in this context, fostering collaborative efforts to establish a unified materials data space. The implementation of digital workflows and Semantic Web technologies, such as ontologies and knowledge graphs, facilitates the semantic integration of heterogeneous data and tools at multiple scales. Central to this effort is the prototyping of a knowledge graph that employs application ontologies tailored to specific data domains, thereby enhancing semantic interoperability. The collaborative approach of the Initiative's community provides significant support infrastructure for understanding and implementing standardized data structures, enhancing the efficiency of data-driven processes in materials development and discovery. Insights and methodologies developed via the MaterialDigital Initiative emphasize the transformative potential of ontology-based approaches in materials science, paving the way toward simplified integration into a unified, consolidated data space of high value.

This manuscript describes the accelerated development of an ontology for microscopy in materials science and engineering, leveraging natural language processing (NLP) techniques. Drawing from a comprehensive corpus comprising over 14 k contributions to the Microscopy and Microanalysis conference series, we employed two neural network-based algorithms for NLP. The goal was to semiautomatically create the Microscopy Ontology (MO) that encapsulates and interconnects the terminology most frequently used by the community. The MO, characterized by its interlinked entities and relationships, is designed to enhance the quality of user query results within NexusLIMS. This enhancement is facilitated through the concurrent querying of related terms and the seamless integration of logical connections.

High-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management.

Knowledge representation in the Materials Science and Engineering (MSE) domain is a vast and multi-faceted challenge: Overlap, ambiguity, and inconsistency in terminology are common. Invariant (consistent) and variant (context-specific) knowledge are difficult to align cross-domain. Generic top-level semantic terminology often is too abstract, while MSE domain terminology often is too specific. In this paper, an approach how to maintain a comprehensive MSE-centric terminology composing a mid-level ontology–the Platform MaterialDigital Core Ontology (PMDco)–via MSE community-based curation procedures is presented. The illustrated findings show how the PMDco bridges semantic gaps between high-level, MSE-specific, and other science domain semantics. Additionally, it demonstrates how the PMDco lowers development and integration thresholds. Moreover, the research highlights how to fuel it with real-world data sources ranging from manually conducted experiments and simulations with continuously automated industrial applications.

The digitalization of materials science and engineering (MSE) is currently leading to remarkable advancements in materials research, design, and optimization, fueled by computer‐driven simulations, artificial intelligence, and machine learning. While these developments promise to accelerate materials innovation, challenges in quality assurance, data interoperability, and data management have to be addressed. In response, the adoption of semantic web technologies has emerged as a powerful solution in MSE. Ontologies provide structured and machine‐actionable knowledge representations that enable data integration, harmonization, and improved research collaboration. This study focuses on the tensile test ontology (TTO), which semantically represents the mechanical tensile test method and is developed within the project Plattform MaterialDigital (PMD) in connection with the PMD Core Ontology. Based on ISO 6892‐1, the test standard‐compliant TTO offers a structured vocabulary for tensile test data, ensuring data interoperability, transparency, and reproducibility. By categorizing measurement data and metadata, it facilitates comprehensive data analysis, interpretation, and systematic search in databases. The path from developing an ontology in accordance with an associated test standard, converting selected tensile test data into the interoperable resource description framework format, up to connecting the ontology and data is presented. Such a semantic connection using a data mapping procedure leads to an enhanced ability of querying. The TTO provides a valuable resource for materials researchers and engineers, promoting data and metadata standardization and sharing. Its usage ensures the generation of finable, accessible, interoperable, and reusable data while maintaining both human and machine actionability.

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 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.

The scientific landscape is undergoing rapid transformations with the advent of the digital age which revolutionizes research methodologies. In materials science and engineering, an adoption of modern data management techniques is desirable to maximize the efficiency and accessibility of research efforts. Traditional practices in testing laboratories are usually inadequate for efficient data acquisition and utilization as they lead to local storage and difficulty in publication and correlation with other results. Electronic laboratory notebooks (ELNs) are promising prospects in this respect. Semantic concepts and ontologies enhance interoperability by standardizing experimental data representation. An in-laboratory pipeline seamlessly integrating an ELN with transformation scripts to convert experimental into interoperable data in a machine-actionable format is created in this study as a proof of concept. Tensile test results and the corresponding tensile test ontology are used exemplary. Linking ELN data to semantic concepts enriches the stored information while improving interpretability and reusability. Involving undergraduate students builds a bridge between theory and practice during their training and promotes their digital skills. This study underscores the potential of ELNs and knowledge representations as beneficial means toward improved data management practices that enhance collaborative research and education while ensuring compatibility with evolving standards and technologies.

The application and benefits of Semantic Web Technologies (SWT) for managing, sharing, and (re‐)using of research data are demonstrated in implementations in the field of Materials Science and Engineering (MSE). However, a compilation and classification are needed to fully recognize the scattered published works with its unique added values. Here, the primary use of SWT at the interface with MSE is identified using specifically created categories. This overview highlights promising opportunities for the application of SWT to MSE, such as enhancing the quality of experimental processes, enriching data with contextual information in knowledge graphs, or using ontologies to perform specific queries on semantically structured data. While interdisciplinary work between the two fields is still in its early stages, a great need is identified to facilitate access for nonexperts and develop and provide user‐friendly tools and workflows. The full potential of SWT can best be achieved in the long term by the broad acceptance and active participation of the MSE community. In perspective, these technological solutions will advance the field of MSE by making data FAIR. Data‐driven approaches will benefit from these data structures and their connections to catalyze knowledge generation in MSE.

The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well‐structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer‐processable data triples.

This poster presents the focused ion beam preparation methodologies developed within the framework of the EU funded EURAMET project ADVENT (Advanced Energy-Saving Technology). It summarises the key breakthroughs achieved for various in situ investigation techniques, e.g. in situ experiments at the Synchrotron facility BESSY II (IR-SNOM and XRS), TEM and SMM instrumentation. The created experimental devices from diverse thin-film semiconductor materials paved the way to dynamic structural studies bearing the potential to determine nanoscale correlations between strain and electric fields and, moreover, for the fundamental development of new in situ capabilities.

Ageing and fatigue if energy materials (solar cells and - mirrors, batteries and power electronics is energetically, financially and environmentally relevant. These devices are prone to similar ageing mechanisms that can be addressed with a set of complementary tools based on: X-ray and optical spectroscopy, electrical and near-field optical scanning probe microscopy (SPM), transmission electron microscopy (TEM), and electrical characterization. Due to the complexity of most energy materials, it is often not possible to unambiguously interpret the experimental results. For this reason, the proposed ageing and fatigue tests are in.situ and operand, and Multiphysics modelling is used for interpretation of measurement results.

The combination of high stiffness, strength, and toughness of many biological tissues is achieved through complex 3D arrangement of hard and soft components. While the hard building blocks are associated with the general stiffness of these biocomposite structures, the soft organic constituents provide the necessary flexibility and toughness and are susceptible to moisture uptake. Because many biological materials reside in humid environments, water is an inherent component of their microstructure. Hence, many studies have emphasized the effect of moisture content on mechanical performance of these materials. High toughness is indeed reported in materials, such as bone, teeth, mollusk shells, and glass sponges, when measured in high relative humidities, nevertheless, not much is known about the exact mechanisms that are responsible for this phenomenon. In the present work, newly developed environmentally controlled nanomechanical characterization techniques are employed to probe the prismatic layer in the shell of Pinna nobilis consisting of hard calcitic blocks surrounded by 1 μm thick organic matrix. Using spatially resolved mechanical data, it is demonstrated that water not only strongly affects the mechanical properties of the biocomposite tissue and its constituents but also is an integral part of explicit intrinsic and extrinsic toughening mechanisms revealed in this study.

Biological composite materials are known to be tough, stiff, stable, viscoelastic bodies, that can creep, recover, absorb energy, and filter vibrations. Their multifunctionality is associated with their architectures, which often consist of mineral units surrounded by organic interfaces that play a key role in the performance of the entire composite. However, the confinement and small dimensions of these organic interfaces pose a challenge in measuring their physical properties by direct methods. We propose an indirect, experimental-analytical framework by which to probe the elastic and viscoelastic behavior of an individual interface. We demonstrate this framework on thin organic interfaces in the shell Pinna nobilis, and discuss its possible uses in various other micro- and nanoscale composite systems.

Living organisms are known to form a large variety of mineral-organic composite structures with mechanical properties that combine high stiffness, strength, toughness and perform at different levels of relative humidity (RH). Usually, the organic components are spatially limited to sub-micrometer features and are confined by a much stiffer mineral material. Mechanical characterization of these organic features, whose properties are strongly influenced by RH, presents a real technical challenge. In the present work we demonstrate our ability to measure environmentally dependent static and dynamic mechanical performance of 1 μm thick organic films in the prismatic layer of the mollusc shell Pinna nobilis employing the nanoscale modulus mapping technique. Two recent developments were utilized during the mechanical characterization process: (1) Environmental control during measurements enabled us to perform the modulus mapping in RH ranging from 0 to 98% [1]; (2) The concept of effective mass of the nanoindenter tip/sample configuration enabled us to account for drastic changes in elastic properties of the organic phase with increasing RH [2]. The possibilities and the limitations of this methodology with regards to structural and mechanical properties of the studied prismatic microstructure in the shell will be discussed.

We apply advanced X-ray diffraction techniques at synchrotron microfocus beamlines in order to study the local ultrastructure of biogenic calcite with high spatial and angular resolution. Specifically, we investigate individual calcitic prisms extracted from Pinna nobilis mollusk shells with an aim to shed additional light on the structural aspects of organic/inorganic interfaces. We use annealing at elevated temperatures to destroy intracrystalline organics and measure the same prisms before and after annealing to achieve deeper understanding of the internal organization of these nanobiocomposites. Complementary nanoindentation measurements (also performed before and after annealing) allow us to elucidate the role of intracrystalline organics in increased hardness in pristine prisms and hardness reduction after annealing. We found that removal of intracrystalline organics during annealing facilitates generation of well-oriented lattice defects, which reduce the (006) diffraction intensity and are responsible for the [001]-elongated diffuse streaks nearby diffraction spots. These findings indicate the formation of internal material's discontinuities with smooth and flat interfaces. Such nanodiscontinuities facilitate microcrack propagation under load that explains the reduced hardness of calcitic prisms after annealing.

The morphology and mesostructure of the prismatic layer of a growing mollusc shell is observed by means of high-resolution synchrotron-based tomography and is shown to be fully predicted by classical theories of normal grain growth.

Biological foams show exceptional energy dissipation during impact, despite the limited choice of materials. Based on a thorough characterisation of the structure-function relationship of the pomelo peel on different length-scales, it is safely to assume that the high energy dissipation capacity is realised by a hierarchical structuring of the foam. We aim at transferring these structural features to metal foams to increase their energy dissipation. The modified investment casting process allows us to cast sandwich foam structures with a graded pore-size, and to introduce additional structural features, as for example a long-fibre reinforcement. In-situ CT analyses show the effect of hierarchical levels on the structure behaviour of open-pore metal foams under compression load.