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.

Nanoporous gold, with its hierarchical structure comprising interconnected networks on multiple length scales, poses significant computational challenges for traditional modeling methods. To solve this challenge, this study introduces a physics‐informed recurrent neural network (RNN) to model the homogenized material response of a diamond beam‐based representative volume element representing the lower level of hierarchy (LL), which was integrated as a material subroutine within an upper level (UL) finite element simulation. The RNN predicts the tangent stiffness matrix as a primary output from given strain trajectories. Secondary outputs such as stress, plastic strain, and plastic energy increments are derived through embedded physical relationships, ensuring physical consistency across the outputs. The RNN architecture enforces positive energy dissipation inherently through positive eigenvalues of the tangent stiffness matrix and further through penalization of negative energy increments, promoting thermodynamically consistent predictions. A two‐level hierarchical approach is employed, where the 2D UL model is subjected to uniaxial strain, while multiaxial strain conditions naturally arise locally within the structure. The inclusion of the LL significantly modifies the strain trajectories observed at the UL, while stress trajectories, although maintaining their general trend, experience considerable changes in magnitude. This approach demonstrates the capability to efficiently simulate hierarchical materials, capturing the influence of LL porosity on the UL material behavior while maintaining physical consistency. Additionally, the model allows for straightforward integration into finite element frameworks like Abaqus, offering a computationally efficient method for studying complex hierarchical materials.

In this contribution, we present our Self-Driving Lab (SDL) for Nano and Advanced Materials, that integrates robotics for batched autonomous synthesis – from molecular precursors to fully purified nanomaterials – with automated characterization and data analysis, for a complete and reliable nanomaterial synthesis workflow. By fully automating the processing steps for seven different materials from five representative, completely different classes of nano- and advanced materials (metal, metal oxide, silica, metal organic framework, and core–shell particles) that follow different reaction mechanisms, we demonstrate the great versatility and flexibility of the platform. The system also exhibits high modularity and adaptability in terms of reaction scales and incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence). We discuss the excellent reproducibility of the various materials synthesized on the platform in terms of particle size and size distribution, and the adaptability and modularity that allows access to a diverse set of nanomaterial classes. We also present several key aspects of the central backend that orchestrates the (parallelized) syntheses workflows. One key feature is the resource management or “traffic control” for scheduling and executing parallel reactions in a multi-threaded environment. Another is the interface with data analysis algorithms from in-line, at-line, and off-line measurements. Here, we will give examples of how automatic image segmentation of electron microscopy images with the help of AI can be used for reducing the “data analysis bottleneck” from an off-line measurement. We will also discuss various machine learning (ML) algorithms that are currently implemented in the backend and can be used for ML-guided, closed-loop material optimization in our SDL. Lastly, we will show our recent efforts in making the workflow generation on SDLs more user-friendly by using large language models to generate executable workflows automatically from synthesis procedures given in natural language and user-friendly graphical user interfaces based on node editors that also allow for knowledge graph extraction from the workflows. In this context, we are currently also working on a common description or ontology for representing the process steps and parameters of the workflows, which will greatly facilitate the semantic description and interoperability of workflows between different SDL hardware and software platforms.

Understanding the plastic deformation of nanoporous metals requires a detailed examination of their small-scale microstructural features. In this work, we present a computational study of micropillar compression in single crystal nanoporous gold (NPG) using crystal plasticity. This approach enables a systematic investigation of three key microstructural effects, including ligament size (50 ≤ 𝑙 ≤ 400 nm), solid fraction (0.2 ≤ 𝜑 ≤ 0.3), and initial crystal orientation ([001] and [111] ̄ ), on the plastic response far beyond yielding. After validation against experimental data, the study reveals that, in line with the ’smaller is stronger’ trend, besides the yield strength, the strain hardening rate also increases as ligament size decreases. Moreover, the strain hardening rate follows a power-law scaling with solid fraction, similar to the yield strength. The analysis of two distinct crystal orientations presents findings contrasting with previous assumptions. While the yielding onset remains orientation-independent, as expected, an increase in the strain hardening rate emerges for the harder [11-1] orientation with continued compression. An effect that becomes more pronounced with increasing solid fraction and decreasing ligament size. Under these conditions, harder orientations also amplify local stress heterogeneity. Notably, the stress distribution in NPG is nearly twice as wide as that observed in the single crystal bulk material (𝜑 = 1.0). Compared to the crystal plasticity approach, traditional isotropic plasticity predicts more uniform local stress fields.

Machine learning (ML) has been increasingly utilized to support microstructure characterization and predict mechanical properties. A successful ML model typically requires a comprehensive understanding of existing knowledge, expertise in translating this knowledge into meaningful input features, an effective ML architecture, and robust validation of the trained model. Despite the rapid growth in publications incorporating ML methods in recent years, there is limited literature specifically addressing nanoporous metals. The talk will give an overview on perspectives and pitfalls in modeling of structureproperty relationships using machine learning with focus on various challenges that arise from the specific nature of nanoporous metals including randomness of microstructure, image segmentation, lack of tomography data, feature engineering for property prediction, and implications for plasticity including anisotropic flow and arbitrary multiaxial loading on the lower scale of hierarchy. An outlook will be given on the perspectives of establishing a culture of open data, specifically towards curated data sets needed for training and validation of ML models. Potential use cases are the comparison of data from different sources, mining of more general relationships, and validation of models trained with computer generated data using experimental data.