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 - 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 - Resch-Genger, Ute T1 - How to get it right with photoluminescence measurements in the UV/VIS/NIR N2 - Optical measurements of transparent solutions of organic dyes and semiconductor quantum dots and scattering materials such as luminescent nanocomposites and microparticles and phosphors dispersed in liquid and solid matrices play an important role in energy conversion, solid-state lighting, medical diagnostics, and security barcoding. A key performance parameter is the photoluminescence quantum yield QY, i.e., the number of emitted per number of absorbed photons. QY of transparent luminophore solutions can be obtained relative to a fluorescence QY standard of known QY. Such relative QY measurements require a calibrated spectrofluorometer. Here, we address typical sources of uncertainty for luminescence measurements and relative and absolute QY measurements from the instrument and material side and provide recommendations to circumvent or at least minimize them for transparent and scattering samples in the UV/vis/NIR/SWIR. T2 - Workshop "Photoluminescence Spectroscopy and its application in Materials Research" CY - Vienna, Austria DA - 19.02.2026 KW - Quality assurance KW - Reference material KW - Fluorescence KW - Dye KW - Traceability KW - Metrology KW - Calibration KW - Reference data KW - Reference product KW - Digital certificate KW - NIR KW - Instrument performance validation PY - 2026 AN - OPUS4-65575 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Digital Transformation in Materials Science: PMD Core Ontology (PMDco) 3.0 – Patterns & Applications N2 - The digitalization of Materials Science and Engineering (MSE) demands standardized, interoperable approaches for representing complex experimental and simulation data. To address this challenge, the Platform MaterialDigital Core Ontology (PMDco) provides a mid-level semantic framework that bridges high-level ontologies and domain-specific terminologies. PMDco version 3.0, recently released, introduces significant enhancements based on a full alignment with the Basic Formal Ontology (BFO) as standardized in ISO/IEC 21838-2. This alignment ensures conceptual consistency and facilitates integration across heterogeneous data sources while enabling FAIR-compliant workflows. First addressing the idea behind a mid-level ontology in MSE, this presentation emphasizes its role in harmonizing diverse data models and supporting machine-actionable knowledge representation. New features and design patterns introduced in PMDco 3.0 will be highlighted, which strengthen interoperability and provide reusable modeling structures for common MSE concepts. Practical applications and prototype implementations will be discussed. PMDco has been developed through active community involvement and its future evolution relies on continued collaboration and discussion within the MSE community. Participation is strongly encouraged to ensure that PMDco remains relevant, comprehensive, and widely adopted. More information and opportunities to contribute can be found at materialdigital.de and github.com/materialdigital/core-ontology. T2 - VMAP User Forum 2026 CY - Sankt Augustin, Germany DA - 24.02.2026 KW - Semantic Technology KW - Knowledge Graph KW - PMD Core Ontology KW - Semantic Pattern KW - Machine-actionability PY - 2026 AN - OPUS4-65574 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Crossing Scientific Disciplines with Materials Informatics:� From Atoms to Algorithms N2 - Within this talk, I introduced students from Physics to Materials Informatics. To provide a context for this research, I have introduced the students to BAM and its tasks. I then started to introduce our activity field materials design, including materials acceleration platforms. Then, I explained how simulations speed up the materials searches as parf of materials acceleration platforms. T2 - jDPG Jena Meeting - Poland exchange CY - Jena, Germany DA - 26.02.2026 KW - Automation KW - Machine Learning KW - Materials Design KW - Batteries KW - Workflows PY - 2026 AN - OPUS4-65589 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Advancing Digital Workflows in Materials Science: The Role of PMDco in Data Integration and Semantic Representation N2 - The field of Materials Science and Engineering (MSE) is undergoing a transformative shift towards digitalization, emphasizing the need for structured and interoperable data management. The Platform MaterialDigital Core Ontology (PMDco), now in version 3.0, addresses these challenges by providing a robust mid-level semantic framework. PMDco bridges the gap between abstract high-level ontologies, such as the Basic Formal Ontology (BFO) standardized in ISO/IEC 21838-2, and highly specific domain terminologies to ensure consistency and interoperability across diverse MSE applications. Developed through MSE community-based curation, PMDco facilitates the integration of real-world data from experiments, simulations, and industrial processes. This presentation will explore PMDco's role in enabling advanced digital workflows and its integration into demonstrators within the Platform MaterialDigital (PMD) initiative. Highlighted use cases include the semantic representation of tensile test data in compliance with ISO 6892-1:2019-11, utilizing the corresponding tensile test ontology (TTO) built on PMDco. Through an electronic laboratory notebook (ELN), data from experiments performed by undergraduate students were transformed into machine-actionable knowledge graphs, demonstrating the potential for education and fully digitalized experimental procedures. Additionally, a possible extension of PMDco as a linking point for semantically representing simulation data will be presented, aligning with the focus of VMAP. This includes linking experimental, simulation, and computational datasets to create comprehensive, FAIR-compliant knowledge ecosystems. By showcasing best practices in data acquisition, semantic integration, and knowledge graph generation, this presentation underscores PMDco’s versatility and its critical role in advancing digital MSE workflows. T2 - VMAP User Forum 2025 CY - Sankt Augustin, Germany DA - 18.02.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability KW - PMD Core Ontology PY - 2025 AN - OPUS4-62607 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schlegel, Moritz-Caspar T1 - Umsetzung der Ökodesign-Verordnung - Wie wird die Beteiligung der relevanten Stakeholder organisiert? N2 - Überblick über die nationale und europäische Stakeholder-Konsultation zu EU-Rechtssetzungsprozessen zur Ökodesign-Rahmenverordnung (ESPR) und diesbezüglicher Produktverordnungen, sowie Benennung von Möglichkeiten der Stakeholder-Koordination durch Normungsorganisationen. T2 - BDI AK Circular Economy CY - Online meeting DA - 18.03.2025 KW - Circular economy KW - Rechtssetzung KW - Normung KW - Politikberatung KW - Ecodesign PY - 2025 AN - OPUS4-62735 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -