TY - CONF A1 - Huber, Norbert T1 - Machine learning in materials science and engineering – best practice, perspectives and pitfalls N2 - 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. T2 - WW-Colloquium Department of Materials Science and Engineering CY - Nuremberg, Germany DA - 13.01.2026 KW - Machine learning KW - Nanoindentation KW - Material testing KW - Nanoporous metals PY - 2026 AN - OPUS4-65358 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ruehle, Bastian T1 - MINERVA-OS: The Orchestrator of our SDL for Nano and Advanced Materials Synthesis N2 - We present our SDL "Minerva" and its' Orchestrator "Minerva-OS". We discuss the general architecture of the orchestrator, the problems our orchestration solution solves, associated objectives, and success criteria. We demonstrate how a user would interact with it and give examples of what it has already been used for. We also provide some explanation of the available and planned features, and how workflows/experiments are represented. Lastly, we discuss key technical challenges we faced during development. T2 - Orchestration Alignment Virtual Workshop CY - Online meeting DA - 20.01.2026 KW - Self Driving Labs KW - Materials Acceleration Platforms KW - Workflows KW - Orchestration PY - 2026 AN - OPUS4-65422 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Robust Data Generation, Heuristics and Machine Learning for Materials Design N2 - My talk covered, among other things, robust data generation for machine learning. It showed how heuristics can be used within machine learning models and how they might also be extracted from machine learning models. Beyond this, I showed an automated pipeline for training machine learning potentials. T2 - Workshop on AI in Sustainable Materials Science CY - Düsseldorf, Germany DA - 27.01.2026 KW - Automation KW - Digitalisation KW - Materials Design KW - Thermal Conductivity KW - Chemical bonding KW - Materials Acceleration Platforms PY - 2026 AN - OPUS4-65427 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Robust Data Generation, Heuristics and Machine Learning for Materials Design N2 - My talk covered, among other things, robust data generation for machine learning. It showed how heuristics can be used within machine learning models and how they might also be extracted from machine learning models. Beyond this, I showed an automated pipeline for training machine learning potentials. T2 - Seminar Gruppe Stephan Roche CY - Barcelona, Spain DA - 22.01.2026 KW - Automation KW - Machine Learning KW - Materials Acceleration Platforms KW - Thermal Conductivity KW - Phonons KW - Bonding Analysis PY - 2026 AN - OPUS4-65428 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Burkert, Andreas T1 - Grundlagen Spannungsrisskorrosion N2 - Der Vortrag stellt die Grundlagen und Schädigungsmechanismen der Spannungsrisskorrosion an metallenen Werkstoffen vor. Aus dem Zusammenspiel von mechanischer Beanspruchung, dem Korrosionsmedium und den Eigenschaften des Werkstoffs können sich Bedingungen ergeben, welche den Eintritt von SpRK begünstigen können. Speziell wird auf auf die chloridinduzierte Spannungsrisskorrosion und die wasserstoffinduzierte SpRK näher eingegangen. Abschließend werden einige normative Prüfmöglichkeiten vorgestellt. T2 - Korrsosionsschutz in der maritimen Technik CY - Hamburg, Germany DA - 28.01.2026 KW - Spannstahl KW - Spannungsrisskorrosion KW - Nichtrostender Stahl KW - Korrosion PY - 2026 AN - OPUS4-65425 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Rurack, Knut T1 - Functional polymeric coatings for sensory applications N2 - The growing demand for the detection of relevant chemical compounds as close as possible to their point of origin—whether in industrial processes, for (civilian or military) security, or for environmental monitoring—has increased the importance of functional polymers with molecular recognition capabilities. These materials must meet practical requirements arising from very different real-world scenarios in which simple, robust, and field-deployable approaches are needed and in which bioanalytical binders often suffer significant performance losses. Polymer recognition matrices therefore serve as an essential complement to established laboratory-based analytical technologies. The realization of rapid and reliable onsite detection places specific demands on material design: polymeric recognition layers must be produced with minimal thickness, integrated onto suitable carrier media, and designed for both selectivity and operational robustness. This presentation will introduce our work on molecularly imprinted polymers (MIPs) that have been specifically developed for the detection of relevant contaminants such as pesticides and perfluorinated compounds. Beyond these target analytes, the presentation will show how such polymer layers can support robust, sustainable diagnostic concepts, and it will outline generalizable design principles that enable their extension to a broad spectrum of analytical challenges. T2 - Institutskolloquium des Instituts für Chemie, Humboldt Universität zu Berlin CY - Berlin, Germany DA - 28.01.2026 KW - Functional organic materials KW - Responsive polymers KW - Sensing KW - Molecularly imprinted polymers PY - 2026 AN - OPUS4-65435 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Radnik, Jörg T1 - The role of Critical Raw Materials in Advanced Materials for the Energy Transition N2 - Based on the UBA report “Advanced materials for energy transition” by Xenia Knigge and Jörg Radnik the role of critical raw materials is discussed. Critical raw materials are needed in main fields of the energy transition, like photovoltaic, fuel cells, wind energy, and batteries. For the optimisation of the use of these materials different scenarios are discussed like (i) decreasing the needed amount of raw materials, (ii) searching for alternatives, (iii) using technologies which do not require critical raw materials, (iv) increasing the recycling rates, and (v) expanding the raw material sources. T2 - IRISS policy dialogue CY - Online meeting DA - 12.01.2026 KW - Solar Cells KW - Fuel cells KW - Batteries KW - Multi-use materials PY - 2026 AN - OPUS4-65451 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pittner, Andreas T1 - Adaptive manufacturing strategies for DED-Arc: Case studies on high-strength aluminium alloys and gas-turbine blade repair N2 - In this presentation, we showcase BAM’s current research activities on DED-Arc/M processes, covering both the manufacture of novel high-strength aluminium alloys and the development of automated repair strategies for gas turbine blades, with a focus on robust process control and repeatable component quality. T2 - 12. Wissenschaftliches Kolloquium im Rahmen des SFB/TRR 375 "Multifunktionale Hochleistungskomponenten aus hybriden porösen Materialien" CY - Online meeting DA - 05.02.2026 KW - DED-Arc KW - Automation KW - Quality assessment PY - 2026 AN - OPUS4-65472 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Robust Data Generation, Heuristics and Machine Learning for Materials Design N2 - This talk first introduces students to the Materials Acceleration Platforms and Advanced Materials Characterization at BAM. Then, it motivates high-throuhgput screening for materials discovery and advanced materials simulations based on these core topics. Then four different research studies are presentend: evaluation of generative models, synthesizability prediction via PU learning, acceleration of materials property predictions with bonding analysis and advanced materials simulations supported by automatically trained machine learning potentials. T2 - Guest Lecture in MSE 403/1003, a Seminar in the Curriculum of the University of Toronto CY - Online meeting DA - 13.02.2026 KW - Automation KW - Materials Acceleration Platforms KW - Machine Learning KW - Workflows KW - Phonons KW - Bonding Analysis KW - Thermal Conductivity PY - 2026 AN - OPUS4-65514 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hertwig, Andreas T1 - How can ellipsometry help us to understand ultrashort pulse laser material processing? N2 - Laser processing of materials is a key technology in many fields of modern production. Still, more work is needed to understand the mechanisms behind laser material interaction. Recently, ellipsometry has become a valuable tool for investigating materials pre and post laser irradiation. Imaging ellipsometry and time-resolved methods boost this work and open up the path to a much better understanding of light interaction with solid state surfaces. We use spectroscopic single-spot and imaging ellipsometry to investigate treated and untreated surfaces. The goal is to determine changes in the material dielectric function, as well as geometric changes in surface layers (oxidation, modification, ablation, blistering). A good knowledge of bulk dielectric functions is often needed to model processes caused by laser irradiation. Imaging ellipsometry can play an important role in the post-analysis of laser treated sites on surfaces if combined with other methods such as IR-spectroscopy, SEM, topometric methods, Raman-spectroscopy, and XPS. In this work, we present the results of several studies to investigate laser-treated materials by means of ellipsometry. We cover the following topics: • Ablation of amorphous carbon layers, role of intra-layer interference effects on the result • Amorphisation behaviour of c-Si, depending on the crystal orientation, • Understanding the role of bulk and surface properties on ablation behaviour of classical and multicomponent high-entropy metals, T2 - 13th Workshop on Spectroscopic Ellipsometry CY - Genova, Italy DA - 09.02.2026 KW - Ellipsometry KW - Laser Ablation KW - Femtosecond Technology KW - Surface and Thin Layer Technology KW - Laser Surface processing PY - 2026 AN - OPUS4-65521 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -