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Organisationseinheit der BAM
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.
Machine learning in materials science and engineering – best practice, perspectives and pitfalls
(2026)
Machine learning (ML) is increasingly utilized to support the data driven analysis of relationships in multidimensional parameter spaces, ideally as an entry point for a more general phenomenological or physics-based model development. Applications include both forward and inverse problems as well as forward problems, for example parameter identification or modeling of structure-property relationships.
The talk will give an overview over a variety of solutions that benefit from the capability of artificial neural networks to approximate and interpolate complex relationships that are represented by a set of sparse data. The reason behind is that numerical simulations as well as experiments do often not allow to generate enough data such that the data set is not sufficient for a deep-learning approach in connection with the complexity of the problem at hand.
After a short introduction to artificial neural networks along with recommendations for data generation and feature engineering, the talk will cover a range of examples from nanoindentation and material parameter identification, the improvement of characterization techniques by ML correction methods towards recent problems in the prediction of structure-property relationships for materials with complex microstructure. All these examples have in common that a successful ML model typically requires a comprehensive understanding of existing knowledge, expertise in translating this knowledge into meaningful input features, a compact ML architecture, and robust validation of the trained model. The talk will conclude with the example of nanoporous metals that demonstrates the importance of high-quality and bias-free data for the applicability and trustworthiness of the trained model, also emphasizing the need for a culture of open data, specifically towards curated data sets for training and validation of ML models.
Microstructure and orientation effects on microcompression-induced plasticity in nanoporous gold
(2026)
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.
Datasets for structural and mechanical properties of nanoporous networks from FIB reconstruction
(2025)
This dataset includes 3D tomographic reconstruction image files, volume mesh files for finite element simulations, and data on the structural and mechanical properties of nanoporous gold (NPG) structures. It serves as a supplement to a dataset paper, with the corresponding DOI provided in the “Related Identifiers” section. Detailed descriptions of the data, as well as the procedures for their preparation and curation, are presented in that paper.
The base material, nanoporous gold, was fabricated via a dealloying process and has a solid fraction of approximately 0.30. NPG samples with ligament sizes ranging from 20 nm to 400 nm were prepared through dealloying and subsequent thermal annealing. Tomographic TIFF files were obtained via Focused Ion Beam/Scanning Electron Microscopy (FIB/SEM) 3D reconstruction, with the procedure detailed in Philosophical Magazine (2016, 96(32–34), 3322–3335).
Based on the 3D image data, new simulations and analyses were performed. The resulting structural and mechanical property data of nanoporous gold are reported for the first time in the dataset paper and are archived here. This dataset provides a valuable database for the study of nanoporous network materials and can be reused for numerical simulations, additive manufacturing, and machine learning applications within the materials science community.
Perspectives and pitfalls in modeling of structure-property relationships using machine learning
(2025)
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.
Datasets for structural and mechanical properties of nanoporous networks from FIB reconstruction
(2025)
This dataset paper presents a comprehensive archive of 3D tomographic reconstruction image files, volume mesh files for finite element simulations, and tabulated structural and mechanical properties data of nanoporous gold structures. The base material is nanoporous gold, fabricated using a dealloying process, with a solid fraction of approximately 0.30. The NPG samples with ligament sizes ranging from 20 nm to 400 nm were prepared by dealloying and by controlling the thermal annealing process. The original data consist of tomographic TIFF files acquired through Focused Ion Beam/Scanning Electron Microscopy (FIB/SEM) 3D reconstruction, as detailed in Philosophical Magazine 2016 96 (32-34), 3322-3335. At each ligament size, six sets of 3D tomographic images were obtained from different regions of the same sample to ensure representative data. New simulations and analyses were conducted based on the 3D image data. The resulting structural and mechanical property data of nanoporous gold are reported for the first time in this dataset paper. Volume meshing of the 3D reconstructed data was performed using Simpleware software. Structural parameters, including surface area, solid volume, and solid volume fraction of the nanoporous network, were extracted from the meshed volumes. Structural connectivity was assessed from the 3D microstructures. The meshed volumes were then used as input for finite element simulations performed in Abaqus to evaluate mechanical responses under uniaxial compression along all three principal axes respectively. From the resulting stress–strain curves, the Young’s modulus and yield strength of each structure were determined. Both elastic and plastic Poisson’s ratios were analyzed from true strain increments. This dataset includes the 3D tomographic images, corresponding volume mesh files, mechanical behavior data and tables summarizing the structural and mechanical properties. The archived data serve as a database for nanoporous network materials and can be reused for numerical simulations, additive manufacturing, and machine learning applications within the materials science community. All files are openly accessible via the TORE repository at https://doi.org/10.15480/882.15230
Micropillar compression testing is essential for understanding bulk metal plasticity at small scales and has emerged as a key technique for evaluating nanoporous metals like nanoporous gold (NPG). To support experimental design, we present a computational plasticity study on single crystal NPG micropillars, systematically examining four extrinsic factors: pillar height-to-diameter ratio, taper angle, friction coefficient, and misalignment angle. The study reveals that NPG exhibits similar trends to its bulk counterpart but is less prone to post-yield buckling in unstable crystal orientations. For optimal NPG pillar stability, an aspect ratio of is recommended and a moderate taper angle to prevent artificial stiffening and yielding. Even minimal friction enhances stability, while buckling is mainly governed by misalignment, requiring to also avoid underestimating the elastic modulus.
The software backend that controls the robotic hardware and runs the synthesis workflows is a very important component of any Self-Driving Lab (SDL). On the one hand, it has to deal with orchestrating and managing complex and task-specific hardware through low-level communication protocols and plan and use the available resources as efficiently as possible while executing (parallelized) workflows, on the other hand, it is the interface the users use to communicate with this highly complex platform, and as such, it needs to be as helpful and user-friendly as possible. This includes the AI-aided experimental design in which the system helps the user to decide which experiment to run next, providing automated data analysis from characterization measurements, and offering easy to understand tools and graphical user interfaces for generating the workflows that are executed on the platform. Lastly, the specificity of the workflows and their dependence on the hardware and software of the SDLs necessitates a common description or ontology for making them easily interchangeable and interoperable between different platforms and labs.
In this contribution, we present several key aspects of “Minerva-OS”, the central backend that orchestrates the syntheses workflows of our SDL for Nano- and Advanced Materials Syntheses [1]. 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 [2] can be used for reducing the “data analysis bottleneck” from an off-line measurement. We will also discuss, compare, and show benchmarks of 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 [3] 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 an ontology for representing the process steps of the workflows, which will greatly facilitate the semantic description and interoperability of workflows between different SDL hardware and software platforms.
The development of nanoporous metals and metallic composites through dealloying processes presents significant opportunities in materials engineering. However, designing multicomponent precursor alloys and establishing corresponding processing methods that yield predictable compositions and nanostructures remain a complex challenge. The talk explores how machine learning (ML)-augmented computational and experimental methodologies can tackle these challenges by predicting precursor alloy compositions, final nanoporous structures, and mechanical properties, while integrating ML-enabled autonomous experimentation for material design and quantification. Recent advancements in applying ML to nanostructured materials design will be touched along with techniques from other nanomaterial designs that can be adapted for improved control over morphological and compositional outcomes in nanoporous and nanocomposite materials. The focus of the talk will be set on ML-driven approaches to microstructure characterization and mechanical property prediction, modeling, and advanced imaging techniques such as three-dimensional nanotomography. Finally, the talk outlines future directions for ML-enhanced materials science, emphasizing the exploration of high-dimensional parameter spaces and the incorporation of materials kinetics into processing and property evaluation, ultimately advancing the design of nanoporous structures and materials science.
In an era where digital transformation is reshaping every facet of our lives, the field of material science and engineering is no exception. This presentation delves into the exciting journey of material and component testing, tracing its path from the confines of the laboratory to the vast expanse of real-world applications, with a particular focus on enhancing safety and reliability. We will explore how modern techniques are pushing the boundaries of conventional material testing, revealing new dimensions of material behavior and performance that are crucial for ensuring safety. The integration of in situ quality control within production processes is revolutionizing manufacturing, ensuring unparalleled precision and reliability, which are essential for maintaining high safety standards.
As we venture into the realm of Structural Health Monitoring (SHM), discover how cutting-edge technologies are being deployed in field applications, from infrastructure to renewable energy sectors, to monitor and ensure the safety and integrity of critical systems. The presentation will also offer a glimpse into the future, where centralized systems for research data management and innovative digital initiatives promise to transform the landscape of material testing and analysis, further bolstering safety measures. Join us to uncover how these advancements are not only enhancing safety and reliability but also paving the way for a digitally interconnected world.