TY - CONF A1 - Ertural, Christina T1 - Vibe Check via Machine Learning: Testing the Prototype N2 - Vibrational properties play a key role in determining the stability and thermal conductivity behaviour of materials. The quasi- harmonic approximation gives insight into the phononic properties of a compound, but in the established way, i.e. density functional theory based methods, it takes many calculation steps and consumes a lot of resources to arrive at the desired results. Machine learning (ML) trained interatomic potentials (e.g. Gaussian approximation potential, GAP) pose an alternative to the traditional computation way of phonons. We develop a Python code based workflow which combines automation tools like atomate2 with ML to ease providing interactomic potentials for (quantum chemical) computations and databases. T2 - #RSCPoster Twitter conference 2023 CY - Online meeting DA - 28.02.2023 KW - Interatomic potentials KW - Machine learning KW - Phonons KW - Workflow PY - 2023 UR - https://twitter.com/cer5814012/status/1630547004462858240 AN - OPUS4-57059 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hickel, Tilmann T1 - Constructing defect phase diagrams from ab initio calculations N2 - Thermodynamic bulk phase diagrams have become the roadmap used by researchers to identify alloy compositions and process conditions that result in novel materials with tailored microstructures. On the other hand, recent progress in experimental atomic-scale characterization techniques allows one to study the local chemical composition at individual defects such as interfaces, grain boundaries, dislocations and surfaces. They show that changes in the alloy composition can drive not only transitions in the bulk phases present in a material, but also in the concentration and type of defects they contain. Defect phase diagrams, using chemical potentials as thermodynamics variables, provide a natural route to study these chemically driven defects. Our results show, however, that the direct application of thermodynamic approaches can fail to reproduce the experimentally observed defect formation. Therefore, we extend the concept to metastable defect phase diagrams to account for kinetic limitations that prevent the system from reaching equilibrium. We use ab initio calculations based on density functional theory to quantify the extension of regions where defect formation is expected. We successfully applied this concept to explain the formation of large concentrations of planar defects in supersaturated Fe2Nb Laves phases. In addition, we identify in a joint study with experiments conditions and structures in Mg-Al-Ca alloys for defect phase occurrence. The concept offers new avenues for designing materials with tailored defect structures. T2 - Materials Science Colloquium CY - Uppsala, Sweden DA - 25.04.2024 KW - Ab initio simulations KW - Grain boundaries KW - Defect phase diagram KW - Laves phases KW - Workflow PY - 2024 AN - OPUS4-62731 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Janssen, Jan A1 - George, Janine A1 - Geiger, Julian A1 - Bercx, Marnik A1 - Wang, Xing A1 - Ertural, Christina A1 - Schaarschmidt, Joerg A1 - Ganose, Alex M. A1 - Pizzi, Giovanni A1 - Hickel, Tilmann A1 - Neugebauer, Joerg T1 - A Python workflow definition for computational materials design N2 - Numerous Workflow Management Systems (WfMS) have been developed in the field of computational materials science with different workflow formats, hindering interoperability and reproducibility of workflows in the field. To address this challenge, we introduce here the Python Workflow Definition (PWD) as a workflow exchange format to share workflows between Python-based WfMS, currently AiiDA, jobflow, and pyiron. This development is motivated by the similarity of these three Python-based WfMS, that represent the different workflow steps and data transferred between them as nodes and edges in a graph. With the PWD, we aim at fostering the interoperability and reproducibility between the different WfMS in the context of Findable, Accessible, Interoperable, Reusable (FAIR) workflows. To separate the scientific from the technical complexity, the PWD consists of three components: (1) a conda environment that specifies the software dependencies, (2) a Python module that contains the Python functions represented as nodes in the workflow graph, and (3) a workflow graph stored in the JavaScript Object Notation (JSON). The first version of the PWD supports directed acyclic graph (DAG)-based workflows. Thus, any DAG-based workflow defined in one of the three WfMS can be exported to the PWD and afterwards imported from the PWD to one of the other WfMS. After the import, the input parameters of the workflow can be adjusted and computing resources can be assigned to the workflow, before it is executed with the selected WfMS. This import from and export to the PWD is enabled by the PWD Python library that implements the PWD in AiiDA, jobflow, and pyiron. KW - Automation KW - Workflow KW - Materials Design KW - Multi-scale simulation KW - Digitalization PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-632328 DO - https://doi.org/10.48550/arXiv.2505.20366 SP - 1 EP - 12 AN - OPUS4-63232 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Müller, Ralf 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 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 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 - JOUR A1 - Rosen, Andrew S. A1 - Gallant, Max A1 - George, Janine A1 - Riebesell, Janosh A1 - Sahasrabuddhe, Hrushikesh A1 - Shen, Jimmy-Xuan A1 - Wen, Mingjian A1 - Evans, Matthew L. A1 - Petretto, Guido A1 - Waroquiers, David A1 - Rignanese, Gian-Marco A1 - Persson, Kristin A. A1 - Jain, Anubhav A1 - Ganose, Alex M. T1 - Jobflow: Computational Workflows Made Simple N2 - We present Jobflow, a domain-agnostic Python package for writing computational workflows tailored for high-throughput computing applications. With its simple decorator-based approach, functions and class methods can be transformed into compute jobs that can be stitched together into complex workflows. Jobflow fully supports dynamic workflows where the full acyclic graph of compute jobs is not known until runtime, such as compute jobs that launch other jobs based on the results of previous steps in the workflow. The results of all Jobflow compute jobs can be easily stored in a variety of filesystem- and cloud-based databases without the data storage process being part of the underlying workflow logic itself. Jobflow has been intentionally designed to be fully independent of the choice of workflow manager used to dispatch the calculations on remote computing resources. At the time of writing, Jobflow workflows can be executed either locally or across distributed compute environments via an adapter to the FireWorks package, and Jobflow fully supports the integration of additional workflow execution adapters in the future. KW - Automation KW - Workflow KW - Computational Materials Science KW - Computations KW - Software PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-593104 DO - https://doi.org/10.21105/joss.05995 VL - 9 IS - 93 SP - 1 EP - 7 PB - The Open Journal AN - OPUS4-59310 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Roters, F. A1 - Aslam, A. A1 - Bai, Y. A1 - Büschelberger, M. A1 - Bulert, K. A1 - Butz, A. A1 - Hickel, Tilmann A1 - Jogi, T. A1 - Klitschke, S. A1 - Martin, M. A1 - Meyer, L.-P. A1 - Morand, L. A1 - Nahshon, Y. A1 - Radtke, N. A1 - Saikia, Ujjal A1 - Trondl, A. A1 - Wessel, A. A1 - Zierep, P. A1 - Helm, D. T1 - StahlDigital: Ontology-Based Workflows for the Steel Industry N2 - The innovative strength of the steel industry is based on the mastery of microstructure–property relationships. Digital workflows can largely contribute to this aim by making the complexity of workflows reproducible and their execution user independent. In this work, the tools and workflows developed in the project StahlDigital as part of the German MaterialDigital initiative are presented and application examples are provided. A steel ontology builds the foundation for data collection and storage as well as for the semantic description of experimental and simulation data processing workflows. A dataspace powered by the Dataspace Management System (DSMS) digitally represents such workflows and manages data along them in a Findable, Accessible, Interoperable, Reusable manner aiming to derive new knowledge. Experimental data processed via DSMS can be further evaluated and used as input for simulation workflows. The simulations included in such workflows are run by the workflow system pyiron, which has been semantically extended in StahlDigital for this purpose. KW - Steel KW - Rolling KW - Multiscale simulation KW - Ontology KW - Workflow PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-627187 DO - https://doi.org/10.1002/adem.202402148 SN - 1527-2648 SP - 1 EP - 13 PB - Wiley VHC-Verlag AN - OPUS4-62718 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Waurischk, Tina T1 - GlasDigital - Datengetriebener Workflow für die beschleunigte Glasentwicklung N2 - Im Projekt GlasDigital sollen digitale Werkzeuge für die Hochdurchsatzentwicklung neuartiger Glaswerkstoffe erarbeitet werden. Dies soll durch die Kombination robotischer Syntheseverfahren mit selbstlernenden Maschinen und ihre Einbindung in eine Ontologie-basierte digitale Infrastruktur realisiert werden. T2 - 4. Fachsymposium der Fachgruppe Glasapparatebauer CY - Mitterteich, Germany DA - 13.06.2024 KW - Glas KW - Ontology KW - Data Space KW - Simulation KW - Workflow KW - Robotische Schmelzanlage PY - 2024 AN - OPUS4-60388 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Waurischk, Tina T1 - Digital infrastructure 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. 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. This includes software tools for data mining as well as property and process modelling. The presentation provides an overview of how all these tools merge into a digital infrastructure and illustrates their usability using examples. All infrastructural parts were developed by a consortium consisting of 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) as part of a joint project of the German research initiative MaterialDigital. T2 - 97. Glass-Technology Conference CY - Aachen, Germany DA - 27.05.2024 KW - Glass KW - Robotic melting KW - Ontologie KW - Simulation KW - Workflow KW - Data Space KW - Digital Twin PY - 2024 AN - OPUS4-60387 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -