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.
Markus Schilling, P. von Hartrott, Jörg Waitelonis et al.· Advanced Engineering Materia...· 0 citations
Gaining understanding of process-structure-property relationships in materials at a mechanistic level relies on correlative microscopy workflows. These workflows, in turn, fundamentally depend on image matching, i.e., a computer vision task with the objective of finding point correspondences in pairs of images. Matching models are difficult to evaluate quantitatively in the materials field due to a shortage of representative benchmark datasets. Nonetheless, prior research indicates that traditional rule-based image matching techniques such as the surface-invariant feature transform (SIFT) currently fall short on such matching tasks. We present a dataset for cross-modal image matching and data fusion in the materials microscopy domain, which we coin
AmalgaMatch
, to support model benchmarking and fine-tuning efforts. All images are micrographs captured using the most widely applied imaging techniques in materials science including light-optical, scanning electron, and transmission electron microscopy, as well as electron backscatter diffraction (EBSD). Therein, various detectors and imaging modes are employed to capture micrographs of diverse materials. While the majority of images are raw images, some underwent typical processing routes using digital image correlation or EBSD indexing. Common regions in image pairs are populated with hand-annotated keypoint correspondences. While mutual information is limited in cross-modal, multi-scale image pairs, we relied on characteristic defects such as dislocations, grain boundaries, triple junctions, inclusions, pores or topographic features for annotation. Furthermore, the dataset is divided into groups, defined by distinct registration use cases, and further into subsets, defined by the imaged material. The dataset covers many typical use cases for image matching in materials science, including slip partitioning, dislocation characterization, and surface fractography. In total, it comprises 6 groups and 19 subsets with 35 scenes and 187 annotated image pairs to support autonomous multimodal materials data fusion. For each image, we provide structured metadata to facilitate training of hybrid matching models which process textual alongside image-based inputs to improve the matching quality and robustness. A formal ontological model for correlative microscopy and image matching processes is proposed to express image contents, relationships, and transformations through knowledge graphs and to enable aligning with FAIR data principles.
Ali Riza Durmaz, James D. Lamb, Kamilla Zaripova et al.· Scientific Data· 1 citation
