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 for materials. More and more frequently, they are used as features in machine learning. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying these 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 and for machine-learning approaches. The latter relates to a general trend toward automation in density functional-based materials science. The lecture will demonstrate how our tools, that assess local atomic environments, helped to test and develop heuristics and design rules and an intuitive understanding of materials. T2 - 2023 MRS Fall Meeting & Exhibit CY - Boston, Massachusetts, USA DA - 26.11.2023 KW - Automation KW - Machine learning KW - Materials Understanding KW - Magnetism KW - Phonons PY - 2023 AN - OPUS4-59002 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lisec, Jan T1 - When drowning in data - start swimming N2 - A presentation on the subject of Automatic Data Processing Skills in Chemistry to Enhance Scientific Research. T2 - 7th PhD Seminar of the German Working Group for Analytical Spectroscopy (DAAS) in the GDCh Division of Analytical Chemistry CY - Berlin, Germany DA - 15.11.2023 KW - Data KW - Automation KW - Programming PY - 2023 AN - OPUS4-58902 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Automated quantum-chemical bonding analysis with workflow tools 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, other recent workflow contributions to the Materials Project software infrastructure (pymatgen, atomate2) related to phonons and machine-learning potentials will be discussed. T2 - ADIS Workshop 2023 CY - Tegernsee, Germany DA - 29.10.2023 KW - Automation KW - Materials Properties KW - DFT KW - Workflows KW - Machine Learning KW - Thermodynamic Properties PY - 2023 AN - OPUS4-58745 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lemiasheuski, Anton A1 - Bajer, Evgenia A1 - Oder, Gabriele A1 - Göbel, Artur A1 - Hesse, Rene A1 - Bettge, Dirk T1 - Development of an Automated 3D Metallography System (RASI) and its Application in Microstructure Analysis N2 - Many microstructural features exhibit non-trivial geometries, which can only be derived to a limited extent from two-dimensional images. E.g., graphite arrangements in lamellar gray cast iron have complex geometries, and the same is true for additively manufactured materials and three-dimensional conductive path structures. Some can be visualized using tomographic methods, but some cannot be due to weak contrast and/or lack of resolution when analyzing macroscopic objects. Classic metallography can help but must be expanded to the third dimension. The method of reconstructing three-dimensional structures from serial metallographic sections surely is not new. However, the effort required to manually assemble many individual sections into image stacks is very high and stands in the way of frequent application. For this reason, an automated, robot-supported 3D metallography system is being developed at BAM, which carries out the steps of repeated preparation and image acquisition on polished specimen. Preparation includes grinding, polishing and optionally etching of the polished surface. Image acquisition comprises autofocused light microscopic imaging at several magnification levels. The image stacks obtained are then pre-processed, segmented, and converted into 3D models, which in the result appear like microtomographic models, but with high resolution at large volume. Contrasting by classical chemical etching reveals structures that cannot be resolved using tomographic methods. The integration of further imaging and measuring methods into this system is underway. Some examples will be discussed in the presentation. T2 - Euromat 2023 CY - Frankfurt a. M., Germany DA - 04.07.2023 KW - Metallography KW - 3D Reconstruction KW - Roboter KW - Automation KW - Microstructure PY - 2023 AN - OPUS4-58202 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bayerlein, Bernd T1 - PMDco - Platform MaterialDigital Core Ontology N2 - The PMD Core Ontology (PMDco) is a comprehensive set of building blocks produced via consensus building. The ontological building blocks provide a framework representing knowledge about fundamental concepts used in Materials Science and Engineering (MSE) today. The PMDco is a mid-level ontology that establishes connections between narrower MSE application ontologies and domain neutral concepts used in already established broader (top-level) ontologies. The primary goal of the PMDco design is to enable interoperability between various other MSE-related ontologies and other common ontologies. PMDco’s class structure is both comprehensive and extensible, rendering it an efficient tool to structure MSE knowledge. The PMDco serves as a semantic middle-layer unifying common MSE concepts via semantic mapping to other semantic representations using well-known key terms used in the MSE domain. The PMDco enables straight-forward documentation and tracking of science data generation and in consequence enables high-quality FAIR data that allows for precise reproducibility of scientific experiments. The design of PMDco is based on the W3C Provenance Ontology (PROV-O), which provides a standard framework for capturing the production, derivation, and attribution of resources. Via this foundation, the PMDco enables the integration of data from various data origins and the representation of complex workflows. In summary, the PMDco is a valuable advancement for researchers and practitioners in MSE domains. It provides a common MSE vocabulary to represent and share knowledge, allowing for efficient collaboration and promoting interoperability between diverse domains. Its design allows for the systematic integration of data and metadata, enabling seamless tracing of science data. Overall, the PMDco is a crucial step towards a unified and comprehensive understanding of the MSE domain in general. T2 - FEMS EUROMAT 2023 CY - Frankfurt a. M., Germany DA - 03.09.2023 KW - Knowledge Representation KW - Ontology KW - Semantic Interoperability KW - FAIR KW - Automation PY - 2023 AN - OPUS4-58197 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Understanding and Machine Learning of Materials Properties with Quantum-Chemical Bonding Analysis N2 - Bonds and local atomic environments are crucial descriptors for material properties. They have been used to create design rules for materials and are used as features in machine learning of material properties. This talk will show how our recently developed tools, that automatically perform quantum chemical bond analysis and enable the study of chemical bonds and local atomic environments, accelerate and improve the development of such heuristics and machine-learned models for materials properties. T2 - Accelerate Conference 2023 CY - Toronto, Canada DA - 22.08.2023 KW - Automation KW - DFT KW - Bonds PY - 2023 AN - OPUS4-58131 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ertural, Christina T1 - Workflows and automation (lecture/tutorial) N2 - A lecture and tutorial on how to use workflows and automation methods for quantum chemical calculations. T2 - Invited lecture CY - Oxford, UK DA - 28.04.2023 KW - Workflows KW - Automation KW - Quantum chemical bonding analysis PY - 2023 AN - OPUS4-57934 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Naik, Aakash A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Philipp A1 - George, Janine T1 - A Quantum-Chemical Bonding Database for Solid-State Materials N2 - Understanding the chemistry and nature of individual chemical bonds is essential for materials design. Bonding analysis via the LOBSTER software package has provided valuable insights into the properties of materials for thermoelectric and catalysis applications. Thus, the data generated from bonding analysis becomes an invaluable asset that could be utilized as features in large-scale data analysis and machine learning of material properties. However, no systematic studies exist that conducted high-throughput materials simulations to curate and validate bonding data obtained from LOBSTER. Here we present an approach to constructing such a large database consisting of quantum-chemical bonding information. T2 - 16th International conference on materials chemistry (MC16) CY - Dublin, Ireland DA - 03.07.2023 KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 AN - OPUS4-57889 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-driven chemical understanding and machine learning of materials properties N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics for materials. More and more frequently, they are used as features in machine learning. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying these 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 and for machine-learning approaches. The latter relates to a general trend toward automation in density functional-based materials science. The lecture will demonstrate how our tools, that assess local atomic environments, helped to test and develop heuristics and design rules and an intuitive understanding of materials. T2 - 16th International conference on materials chemistry (MC16) CY - Dublin, Irland DA - 03.07.2023 KW - Automation KW - Bonding Analysis KW - Materials Informatics PY - 2023 AN - OPUS4-57876 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chambers, Aaron T1 - Using an automation assisted Synthesis to produce functionalized ZIF-8 nanoparticles for effective composite formation N2 - This presentation details the progress of the PhD project so far. It discuss the background of the project such as what is a MOF, ZIF and ZIF composite before discussing the aims and approaches for the work. Then the presentation looks at the data obtained so far and the techniques/syntheses used. T2 - Postgraduate Researcher Symposium, University of Birmingham CY - Online meeting DA - 27.06.2023 KW - Automation KW - Nanoparticles KW - ZIF-8 KW - Composites PY - 2023 AN - OPUS4-57867 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -