TY - CONF A1 - Kromm, Arne T1 - Möglichkeiten zur Optimierung von schweißbedingten Beanspruchungen durch Einsatz von LTT-Zusatzwerkstoffen N2 - Der Vortrag gibt einen Überblick zum Thema Low Transformation Temperature (LTT) Zusatzwerkstoffe. Es wird gezeigt, wie mit einem Experiment unter Nutzung von Sychrotronstrahlung das Phänomen der Umwandlungsplastizität untersucht werden kann. T2 - 12. Vollversammlung des SFB 1120: Summer School 2024 CY - Steinfeld (Kall), Germany DA - 11.06.2024 KW - Eigenspannungen KW - LTT-Zusatzwerkstoffe KW - Hochfester Stahl PY - 2024 AN - OPUS4-60259 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Radnik, Jörg T1 - How to perform reliable xps-measurements? N2 - Reliable measurements are a great challenge in X-ray photoelectron spectroscopy. It will be shown, how to check the performance and calibration of the instrument, the charge compensation, quantification, fitting, and depth profiling. T2 - Course for PhD students at SFB 1073 CY - Göttingen, Germany DA - 23.02.2024 KW - X-ray photoelectron spectroscopy KW - Reproducibility crisis KW - Reliability PY - 2024 AN - OPUS4-59590 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Radnik, Jörg A1 - Stockmann, Jörg Manfred A1 - Knigge, Xenia A1 - Chemello, Giovanni A1 - Hodoroaba, Vasile-Dan A1 - Resch-Genger, Ute A1 - Madkour, S. A1 - Schönhals, Andreas A1 - Reed, B. A1 - Clifford, C. A1 - Shard, A. T1 - (hard) x ray photoelectron spectroscopy as tool in nano-analytics N2 - Selected samples are used to show, that photoelectron spectroscopy is an important tool for measuring nano-objects, especially for determing the chemical composition and structure on the nanosacle. XPS with different energies offers new possibilities in depth-profiling. T2 - SFB 1073 Colloquium CY - Göttingen, Germany DA - 22.02.2024 KW - X-ray photoelectron spectroscopy KW - Core-shell nanoparticle KW - Graphene related 2D materials PY - 2024 AN - OPUS4-59589 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Koch, Claudia T1 - Quality X: Data Spaces for laboratories. How about a proof of concept? N2 - Überblick über die Initiative QI-Digital. Schwerpunkt liegt neben den allgemeinen Zielen und Aktivitäten in verschiedenen Projekten insbesondere auf der Rolle von Datenräumen für die Qualitätsinfrastruktur (QI) und hier insbesondere Labore. Datenräume als Anker einer digitalen QI ermöglichen nicht nur sicheren und souveränen Datenaustausch sondern bieten auch Potential für neue Geschäftsmodelle. T2 - VUP Jahrestagung CY - Saarbrücken, Germany DA - 11.06.2024 KW - Qualitätsinfrastruktur KW - Qualitätssicherung KW - Data Spaces KW - Digitale Transformation KW - Datenräume KW - Digitales Zertifikat KW - DPP PY - 2024 AN - OPUS4-60346 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Koch, Claudia T1 - Eine moderne Qualitätsinfrastruktur: digital und vernetzt N2 - Überblick über die digitale Transformation der Qualitätsinfrastruktur (QI), die Initiative QI-Digital sowie die Aktivitäten der BAM. T2 - 11. Sitzung DGZfP UA Schnittstellen, Dokumentation, Datensouveränität Speicherung, Archivierung CY - Saarbrücken, Germany DA - 11.4.2024 KW - Qualitätsinfrastruktur KW - Qualitätssicherung KW - Zerstörungsfreie Prüfung KW - digitale Transformation KW - Smart Standard KW - Digitales Zertifikat KW - Quality-X PY - 2024 AN - OPUS4-60347 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Müller, Ralf A1 - Schottner, G. A1 - Wondraczek, L. A1 - Sierka, M. A1 - Deubener, J. A1 - Waurischk, Tina A1 - Reinsch, Stefan A1 - Contreras, A. A1 - Diegeler, A. A1 - Kilo, M. A1 - Pan, Z.-W. A1 - Arendt, F. A1 - Chen, Y.-F. A1 - Gogula, S. A1 - Bornhöft, H. T1 - GlasDigital: Data-driven workflow for accelerated glass development N2 - Glasses stand out by their wide and continuously tunable chemical composition and large variety of unique shaping techniques making them a key component of modern high technologies. Glass development, however, is still often too cost-, time- and energy-intensive. The use of robotic melting systems embedded in an ontology-based digital environment is intended to overcome these problems in future. As part of the German research initiative MaterialDigital, the joint project GlasDigital takes first steps in this direction. The project consortium involves the Fraunhofer ISC in Würzburg, the Friedrich Schiller University Jena (OSIM), the Clausthal University of Technology (INW), and the Federal Institute for Materials Research and Testing (BAM, Division Glasses) and aims to combine all main basic components required for accelerated data driven glass development. For this purpose, a robotic high throughput glass melting system is equipped with novel inline sensors for process monitoring, machine learning (ML)-based, adaptive algorithms for process monitoring and optimization, novel tools for high throughput glass analysis and ML-based algorithms for glass design, including software tools for data mining as well as property and process modelling. The talk gives an overview how all these tools are interconnected and illustrates their usability with some examples. T2 - USTV-DGG joint meeting CY - Orleans, France DA - 22.05.2023 KW - Glass KW - Ontology KW - Data Space KW - Workflow KW - Robotic melting PY - 2023 AN - OPUS4-60372 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Müller, Ralf A1 - Diegeler, A. A1 - Schottner, G. A1 - Niebergall, R. A1 - Kilo, M. A1 - Arendt, F. A1 - Chen, Y.-F. A1 - Sierka, M. A1 - Limbach, R. A1 - Pan, Z. A1 - Wondraczek, L. A1 - Gogula, S. A1 - Bornhöft, H. A1 - Deubener, J. A1 - Waurischk, Tina A1 - Reinsch, Stefan T1 - GlassDigital: Digital Infrastructure for Data-Driven High-Throughput Glass Development N2 - Gläser zeichnen sich durch eine breite und kontinuierlich abstimmbare chemische Zusammensetzung sowie einzigartige Formgebungstechniken aus, was sie oft zur Schlüsselkomponente moderner Hochtechnologien macht. Die Glasentwicklung ist jedoch oft noch zu kosten-, zeit- und energieintensiv. Der Einsatz von robotergestützten Schmelzsystemen, eingebettet in eine Ontologie-basierte digitale Umgebung, soll diese Probleme in Zukunft überwinden. Im Rahmen der BMBF Forschungsinitiative MaterialDigital unternimmt das Verbundprojekt GlasDigital „Datengetriebener Workflow für die beschleunigte Entwicklung von Glas“ erste Schritte in diese Richtung. Das Projektkonsortium, an dem das Fraunhofer ISC in Würzburg, die Friedrich-Schiller-Universität Jena (OSIM), die Technische Universität Clausthal (INW) und die Bundesanstalt für Materialforschung und -prüfung (BAM, Fachgruppe Glas) beteiligt sind, will alle wesentlichen Basiskomponenten für eine beschleunigte datengetriebene Glasentwicklung zusammenführen. Zu diesem Zweck wird ein robotergestütztes Hochdurchsatz-Glasschmelzsystem mit neuartigen Inline-Sensoren zur Prozessüberwachung, auf maschinellem Lernen (ML) basierenden adaptiven Algorithmen zur Prozessüberwachung und -optimierung, neuartigen Werkzeugen für die Hochdurchsatz-Glasanalyse sowie ML-basierten Algorithmen zum Glasdesign, Data Mining sowie Eigenschafts- und Prozessmodellierung ausgestattet. Der Vortrag gibt einen Überblick darüber, wie all diese Komponenten miteinander verzahnt sind, und veranschaulicht ihre Nutzbarkeit anhand einiger Beispiele. T2 - HVG-Fortbildungskurs CY - Offenbach, Germany DA - 27.11.2023 KW - Glas KW - Ontology KW - Workflow KW - Simulation KW - Robotic melting PY - 2023 AN - OPUS4-60386 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Müller, Ralf A1 - Niebergall, R. A1 - Schottner, G. A1 - Wondraczek, L. A1 - Sierka, M. A1 - Deubener, J. A1 - Waurischk, Tina A1 - Reinsch, Stefan A1 - Contreras, A. A1 - Diegeler, A. A1 - Kilo, M. A1 - Puppe, F. A1 - Limbach, R. A1 - Pan, Z. A1 - Arendt, F. A1 - Chen, Y.-F. A1 - Gogula, S. A1 - Bornhöft, H. T1 - GlasDigital: Data-driven workflow for accelerated glass development N2 - lasses stand out by their wide and continuously tunable chemical composition and large variety of unique shaping techniques making them a key component of modern high technologies. Glass development, however, is still often too cost-, time- and energy-intensive. The use of robotic melting systems embedded in an ontology-based digital environment is intended to overcome these problems in future. As part of the German research initiative MaterialDigital, the joint project GlasDigital takes first steps in this direction. The project consortium involves the Fraunhofer ISC in Würzburg, the Friedrich Schiller University Jena (OSIM), the Clausthal University of Technology (INW), and the Federal Institute for Materials Research and Testing (BAM, Division Glasses) and aims to combine all main basic components required for accelerated data driven glass development. For this purpose, a robotic high throughput glass melting system is equipped with novel inline sensors for process monitoring, machine learning (ML)-based, adaptive algorithms for process monitoring and optimization, novel tools for high throughput glass analysis and ML-based algorithms for glass design, including software tools for data mining as well as property and process modelling. The talk gives an overview how all these tools are interconnected and illustrates their usability with some examples. T2 - HVG-DGG Fachausschuss I CY - Jena, Germany DA - 03.11.2023 KW - Glass KW - Robotic melting KW - Ontologie KW - Simulation KW - Workflow KW - Data Space PY - 2023 AN - OPUS4-60383 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bayerlein, Bernd A1 - Schilling, Markus A1 - Birkholz, Henk T1 - Digital Transformation in Materials Science: Insights From the Platform MaterialDigital (PMD) N2 - The digital era has led to a significant increase in innovation in scientific research across diverse fields and sectors. Evolution of data-driven methodologies lead to a number of paradigm shifts how data, information, and knowledge is produced, understood, and analyzed. High profile paradigm shifts in the field of materials science (MS) include exploitative usage of computational tools, machine learning algorithms, and high-performance computing, which unlock novel avenues for investigating materials. In these presentations, we highlight prototype solutions developed in the context of the Platform MaterialDigital (PMD) project that addresses digitalization challenges. As part of the Material Digital Initiative, the PMD supports the establishment of a virtual materials data space and a systematic handling of hierarchical processes and materials data using a developed ontological framework as high priority work items. In particular, the mid-level ontology PMDco and its augmentation through application-specific ontologies are illustrated. As part of the conclusion, a discussion encompasses the evolutionary path of the ontological framework, taking into account standardization efforts and the integration of modern AI methodologies such as natural language processing (NLP). Moreover, demonstrators illustrated in these presentations highlight: The integration and interconnection of tools, such as digital workflows and ontologies, Semantic integration of diverse data as proof of concept for semantic interoperability, Improved reproducibility in image processing and analysis, and Seamless data acquisition pipelines supported by an ontological framework. In this context, concepts regarding the application of modern research data management tools, such as electronic laboratory notebooks (ELN) and laboratory information management systems (LIMS), are presented and elaborated on. Furthermore, the growing relevance of a standardized adoption of such technologies in the future landscape of digital initiatives is addressed. This is supposed to provide an additional basis for discussion with respect to possible collaborations. T2 - NIST Seminar on Digital Transformation CY - Gaithersburg, MD, USA DA - 11.06.2024 KW - Digital Transformation KW - Research Data Management KW - Ontology KW - Reusability KW - FAIR PY - 2024 AN - OPUS4-60381 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schlegel, Moritz-Caspar T1 - Circular Economy in der EU - ein neues Verständnis für Produkteffizienz, die EN4555x-Reihe und weitere Beipiele N2 - Übersicht über die Umsetzung des EU Green New Deal, des Circular Economy Action Plan sowie der Sustainable Product Initiative. Ebenfalls erfolgt eine Zusammenfassung der Auswirkungen der Änderungen im EU-Produktrecht auf die Revision des geltenden Rechts und das Zusammenspiel zwischen Rechtssetzung und Normung. T2 - Besuch des Bachelorstudiengangs im Fachbereich Material Knowledge der Fachhochschule Potsdam CY - Berlin, Germany DA - 28.05.2024 KW - Circular Economy KW - ESPR KW - Material efficiency KW - Normung KW - Policy making KW - Labelling PY - 2024 AN - OPUS4-60407 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schlegel, Moritz-Caspar T1 - Rechtsetzungbegleitende Normung zur Circular Economy von Produkten - Beispiel IKT N2 - Überblick über Aktionspläne, Initiativen und Maßnahmen der EU zur Steigerung der Zirkularität von Produkten aus der Informations- und Kommunikationstechnik. Des Weiteren eine Übersicht über relevante nationale und europäische Normungsgremien und die Beteiligung der BAM. T2 - Gründungssitzung des Forschungsfabrik Mikroelektronik Deutschland (FMD) CY - Online meeting DA - 31.01.2024 KW - Circular Economy KW - ESPR KW - Energy labelling KW - Normung KW - Policy making KW - Standardization PY - 2024 AN - OPUS4-60397 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schlegel, Moritz-Caspar T1 - Circular Economy bei der BAM - neu und etabliert zugleich N2 - Übersicht über BAM-Aktivitäten, die ebenfalls der BAM zugeordnet werden können, der Gründung des Aktivitätsfeldes Circular Economy innerhalb der BAM sowie die zukünftige Ausrichtung und Aktivitäten der BAM zu Themen der Circular Economy. T2 - Besuch des Bachelorstudiengangs im Fachbereich Material Knowledge der Fachhochschule Potsdam CY - Berlin, Germany DA - 28.05.2024 KW - Circular Economy PY - 2024 AN - OPUS4-60410 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus A1 - Bayerlein, Bernd A1 - Birkholz, H. T1 - Digital Transformation in Materials Science: Insights from Platform MaterialDigital (PMD), Tensile Test Ontology (TTO), Electronic Lab Notebooks (ELN) N2 - The digital era has led to a significant increase in innovation in scientific research across diverse fields and sectors. Evolution of data-driven methodologies lead to a number of paradigm shifts how data, information, and knowledge is produced, understood, and analyzed. High profile paradigm shifts in the field of materials science (MS) include exploitative usage of computational tools, machine learning algorithms, and high-performance computing, which unlock novel avenues for investigating materials. In these presentations, we highlight prototype solutions developed in the context of the Platform MaterialDigital (PMD) project that addresses digitalization challenges. As part of the Material Digital Initiative, the PMD supports the establishment of a virtual materials data space and a systematic handling of hierarchical processes and materials data using a developed ontological framework as high priority work items. In particular, the mid-level ontology PMDco and its augmentation through application-specific ontologies are illustrated. As part of the conclusion, a discussion encompasses the evolutionary path of the ontological framework, taking into account standardization efforts and the integration of modern AI methodologies such as natural language processing (NLP). Moreover, demonstrators illustrated in these presentations highlight: The integration and interconnection of tools, such as digital workflows and ontologies, Semantic integration of diverse data as proof of concept for semantic interoperability, Improved reproducibility in image processing and analysis, and Seamless data acquisition pipelines supported by an ontological framework. In this context, concepts regarding the application of modern research data management tools, such as electronic laboratory notebooks (ELN) and laboratory information management systems (LIMS), are presented and elaborated on. Furthermore, the growing relevance of a standardized adoption of such technologies in the future landscape of digital initiatives is addressed. This is supposed to provide an additional basis for discussion with respect to possible collaborations. T2 - NIST Seminar Series CY - Gaithersburg, MD, USA DA - 11.06.2024 KW - Semantic Data KW - Plattform Material Digital KW - Digitalization KW - Data Interoperability KW - NIST KW - Tensile Test Ontology KW - Elctronic Lab Notebook PY - 2024 AN - OPUS4-60392 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schlegel, Moritz-Caspar T1 - Stand der Durchführungsmaßnahmen und delegierte Rechtsakte - Zusammenfassung und Ausblick N2 - Übersicht über den Aktuellen Stand der Durchführungsmaßnahmen der EU zur Ökodesign-Richtlinie sowie der delegierten Rechtsakte zum EU Energielabel. Der Fokus liegt auf die Änderungen bzgl. gesetzlicher Anforderungen, die Überwachung durch die Vollzugsbehörden und mögliche diesbezügliche Informationsquellen. T2 - Praxisworkshop Marktüberwachung EVPG / EnVKG CY - Weilburg, Germany DA - 14.05.2024 KW - Ecodesign KW - EU Energy Label KW - Policy making KW - Market suveillance PY - 2024 AN - OPUS4-60409 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-Driven Chemical Understanding with Bonding Analysis N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed. T2 - Otaniemi Center for Atomic-scale Materials Modeling Seminar CY - Aalto, Finland DA - 11.03.2024 KW - Automation KW - Materials Design KW - Bonding Analysis KW - Machine Learning PY - 2024 AN - OPUS4-59671 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - New Opportunities for Data-Driven Chemistry and Materials Science Through Automation N2 - In recent years, many protocols in computational materials science have been automated and made available within software packages (primarily Python-based). This ranges from the automation of simple heuristics (oxidation states, coordination environments) to the automation of protocols, including multiple DFT and post-processing tools such as (an)harmonic phonon computations or bonding analysis. Such developments also shorten the time frames of projects after such developments have been made available and open new possibilities. For example, we can now easily make data-driven tests of well-known rules and heuristics or develop quantum chemistry-based materials descriptors for machine learning approaches. These tests and descriptors can have applications related to magnetic ground state predictions of materials relevant for spintronic applications or for predicting thermal properties relevant for thermal management in electronics. Combining high-throughput ab initio computations with fitting, fine-tuning machine learning models and predictions of such models within complex workflows is also possible and promises further acceleration in the field. In this talk, I will show our latest efforts to link automation with data-driven chemistry and materials science. T2 - MRS Spring CY - Seattle, Washington, USA DA - 21.04.2024 KW - Automation KW - Workflows KW - Chemical Bonding KW - Thermal Properties KW - Materials Design PY - 2024 AN - OPUS4-59982 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - High-throughput and automated bonding Analysis N2 - Talk as a part of the LOBSTER CECAM SCHOOL. This talk introduced all participants to automation tools around the software LOBSTER. T2 - CECAM LOBSTER School CY - Aalto, Finland DA - 12.03.2024 KW - Automation KW - Workflows KW - Bonding Analysis KW - Materials Design KW - Chemically Complex Materials PY - 2024 AN - OPUS4-59672 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Trappe, Volker A1 - Kalinka, Gerhard A1 - Loose, Florian T1 - Advanced lightweight applications – recycling versus reliability and fossil energy footprint N2 - Advanced light weight applications like aircrafts and wind turbine blades are made of fibre reinforced plastics (FRP) with continuous fibre reinforcement and must withstand a high thermo-mechanical cyclic loading. The quality of the fibre matrix interface has a high impact on the fatigue life and was continuously improved over the years since the 50th. The fatigue life of glass fibre reinforced plastics (GFRP) used in aircraft industry is 10 to 100 times higher compared to glass fibre non crimp fabrics used for wind turbine blades. To assure a constant and reliable high quality and strength of reinforcement fibres, synthetic fibre production is state of the art (CF, GF). There is a need for recycling GFRP and CFRP waste due to the upcoming use. Pyrolysis and solvolysis are more expensive than the mechanical route however enable a more sustainable recycling. Natural fibres and recycled synthetic fibres have a high scatter in quality and strength. Hence it is a challenge to optimize the production / recycling processes to get a reliable quality for any demanding (second life) application. Chemical routes for using renewables resources and recycling, is going to be a good approach especially for polymer-matrix systems to get 100% quality (back) compared to the state of the art. Finally, a proper design, life-time extension and repair is preferable to recycling to keep the carbon footprint as low as possible. T2 - 27. INTERNATIONALES DRESDNER LEICHTBAUSYMPOSIUM CY - Dresden, Germany DA - 13.06.2024 KW - Polymer Matrix Composites KW - Carbon Fibre KW - Recycling KW - Circular Economy PY - 2024 UR - https://leichtbausymposium.de/deu/ AN - OPUS4-60683 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hertwig, Andreas A1 - Radnik, Jörg A1 - Ermilova, Elena A1 - Hodoroaba, Vasile-Dan T1 - Accuracy on all scales: Hybrid metrology for micro- and nanomanufacturing N2 - In this presentation, we discuss hybrid metrology and correlative imaging. These techniques are used to improve the design and quality monitoring of nanomaterials used in energy technology and for referencing the properties of nanoparticles. T2 - EMN for Advanced Manufacturing workshop CY - Berlin, Germany DA - 22.05.2024 KW - Nanotechnology KW - Nanoanalytics KW - Correlative Spectroscopy KW - Correlative Imaging PY - 2024 AN - OPUS4-60240 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-driven approaches for materials understanding and design N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in computational materials science and some of our recent contributions will be discussed. T2 - Machine Learning of First Principles Observables CY - Berlin, Germany DA - 08.07.2024 KW - Automation KW - Bonding Analysis KW - Machine Learned Potentials PY - 2024 AN - OPUS4-60660 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -