Open Access

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.

Liquid-metal embrittlement (LME) in Zn-coated steels is traditionally understood as a consequence of liquid Zn penetration along grain boundaries (GBs) during thermo-mechanical processing. However, recent thermodynamic predictions brought to light a massive Zn segregation transition at Fe GBs that suggest a strong driving force for intermetallic phase formation at substantially lower temperatures than the melting point of Zn. Leveraging bulk mechanical testing, high-energy synchrotron diffraction, and transmission electron microscopy, we demonstrate here that embrittling Fe-rich intermetallic grain-boundary phases emerge in an advanced high-strength steel prior to any melting of Zn. Their formation and increasing presence with temperature correlates with severe mechanical degradation. These findings provide consistent evidence that the solid-state formation of Fe-rich Fe-Zn intermetallic phases constitutes an early contributing step to LME in galvanized high-strength steels.

With recent advances in generative machine learning, different models have been adapted to predict novel materials, and new architectures are emerging frequently. While several metrics allow for the rating of individual characteristics (e.g., quality or novelty) of generated crystal structures on an instance level, approaches that evaluate the general performance of generative models for materials prediction are missing. To close this gap, we developed the Transport Novelty Distance (TNovD). This metric evaluates generative models by jointly judging the novelty and quality of all newly generated crystal structures. Thereto, the Wasserstein distance is calculated on an abstract feature space distribution derived from the chemical and physical characteristics of the materials. These features are created by embedding the crystals description with an invariant Graph Neural Network (GNN) that was trained with the InfoNCE loss on the identical set of materials as the generative model. Using contrastive learning allows to not only account for materials themselves, but also for their augmented counterparts and differently sized supercells. Based on the resulting feature space, couplings between generated and train set are calculated and split into a quality and a memorization regime by a threshold. This allows to evaluate quality and novelty simultaneously. The TNovD was tested on various toy experiments for memorization and different cases of crystal structure degeneration. Additionally, we validated it on the MP20 validation set and the WBM substitution dataset. The experiments results demonstrate the TNovD capabilities of detecting both memorization and low-quality materials. Afterwards, we benchmark the performance of several popular material generative models with the MP20 validation data. While introduced for materials, our TNovD framework is domain-agnostic and can be adapted for other areas in the space of chemical compounds, such as images and molecules.

Discovering sustainable glass compositions demands navigating vast chemical spaces—a challenge that conventional experimentation cannot meet efficiently. Here we introduce the Simulation-Calibrated Active Learning Estimator (SCALE), a closed-loop framework uniting high-throughput molecular dynamics (MD), machine learning (ML), and robotic synthesis to bridge the gap between simulation and experiment. Over 42,000 melt–quench MD simulations with compact cells (≈ 200–500 atoms) train ML models that predict density and elastic moduli with cross-validated R2 values up to 0.98. Comparison with 55 robotically synthesized sodium alumino-borosilicate glasses reveals systematic density overestimation of up to 5%. Rather than naively augmenting training data, SCALE iteratively learns a composition-dependent calibration from minimal targeted measurements. The protocol substantially reduces density errors over three experimental iterations of six measurements each and demonstrates the potential for glass optimization with a small number of strategically chosen experiments.

Chronic wounds present alkaline pH commonly associated to frequent infections, making pH monitoring useful to follow the healing process and to guide antibacterial treatments. This work aims to develop a multifunctional natural polymer-based wound dressing embedded with a bioactive glass and a natural dye, enabling healing, antibacterial activity, and colorimetric pH sensing. Preliminary results confirm mechanical flexibility, bioactivity, and measurable pH responsive behavior of the dressings.