TY - JOUR A1 - Ghosh, Sheuly A1 - Ueltzen, Katharina A1 - George, Janine A1 - Neugebauer, Jörg A1 - Körmann, Fritz T1 - Chemical ordering and magnetism in face-centered cubic CrCoNi alloy N2 - AbstractThe impact of magnetism on chemical ordering in face-centered cubic CrCoNi medium entropy alloy is studied by a combination of ab initio simulations, machine learning potentials, and Monte Carlo simulations. Large magnetic energies are revealed for some mixed L12/L10 type ordered configurations, which are rooted in strong nearest-neighbor magnetic exchange interactions and chemical bonding among the constituent elements. There is a delicate interplay between magnetism and stability of MoPt2 and L12/L10 type of order, which may explain opposing experimental and theoretical findings. KW - Chemically complex materials KW - Alloys KW - Magnetism KW - Bonding analysis KW - Machine learning KW - Machine-learned interatomic potentials PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-623114 DO - https://doi.org/10.1038/s41524-024-01439-8 SN - 2057-3960 VL - 10 IS - 1 SP - 1 EP - 10 PB - Springer Science and Business Media LLC AN - OPUS4-62311 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Ghosh, Sheuly A1 - Ueltzen, Katharina A1 - George, Janine A1 - Neugebauer, Jörg A1 - Körmann, Fritz T1 - Chemical ordering and magnetism in face-centered cubic CrCoNi alloy N2 - The impact of magnetism on chemical ordering in face-centered cubic CrCoNi medium entropy alloy is studied by a combination of ab initio simulations, machine learning potentials, and Monte Carlo simulations. Large magnetic energies are revealed for some mixed L12 /L10 type ordered configurations, which are rooted in strong nearest-neighbor magnetic exchange interactions and chemical bonding among the constituent elements. There is a delicate interplay between magnetism and stability of MoPt2 and L12 /L10 type of ordering which may explain opposing experimental and theoretical findings. KW - Bonding analysis KW - Alloys KW - Magnetism KW - Material design PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-600040 DO - https://doi.org/10.21203/rs.3.rs-3978660/v1 SN - 2693-5015 SP - 1 EP - 13 PB - Research Square Platform LLC CY - Durham, NC AN - OPUS4-60004 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bayerlein, Bernd A1 - Waitelonis, Jörg A1 - Birkholz, Henk A1 - Jung, Matthias A1 - Schilling, Markus A1 - v. Hartrott, Philipp A1 - Bruns, Marian A1 - Schaarschmidt, Jörg A1 - Beilke, Kristian A1 - Mutz, Marcel A1 - Nebel, Vincent A1 - Königer, Veit A1 - Beran, Lisa A1 - Kraus, Tobias A1 - Vyas, Akhilesh A1 - Vogt, Lars A1 - Blum, Moritz A1 - Ell, Basil A1 - Chen, Ya‐Fan A1 - Waurischk, Tina A1 - Thomas, Akhil A1 - Durmaz, Ali Riza A1 - Ben Hassine, Sahar A1 - Fresemann, Carina A1 - Dziwis, Gordian A1 - Beygi Nasrabadi, Hossein A1 - Hanke, Thomas A1 - Telong, Melissa A1 - Pirskawetz, Stephan A1 - Kamal, Mohamed A1 - Bjarsch, Thomas A1 - Pähler, Ursula A1 - Hofmann, Peter A1 - Leemhuis, Mena A1 - Özçep, Özgür L. A1 - Meyer, Lars‐Peter A1 - Skrotzki, Birgit A1 - Neugebauer, Jörg A1 - Wenzel, Wolfgang A1 - Sack, Harald A1 - Eberl, Chris A1 - Portella, Pedro Dolabella A1 - Hickel, Tilmann A1 - Mädler, Lutz A1 - Gumbsch, Peter T1 - Concepts for a Semantically Accessible Materials Data Space: Overview over Specific Implementations in Materials Science N2 - This article describes advancements in the ongoing digital transformation in materials science and engineering. It is driven by domain‐specific successes and the development of specialized digital data spaces. There is an evident and increasing need for standardization across various subdomains to support science data exchange across entities. The MaterialDigital Initiative, funded by the German Federal Ministry of Education and Research, takes on a key role in this context, fostering collaborative efforts to establish a unified materials data space. The implementation of digital workflows and Semantic Web technologies, such as ontologies and knowledge graphs, facilitates the semantic integration of heterogeneous data and tools at multiple scales. Central to this effort is the prototyping of a knowledge graph that employs application ontologies tailored to specific data domains, thereby enhancing semantic interoperability. The collaborative approach of the Initiative's community provides significant support infrastructure for understanding and implementing standardized data structures, enhancing the efficiency of data‐driven processes in materials development and discovery. Insights and methodologies developed via the MaterialDigital Initiative emphasize the transformative potential of ontology‐based approaches in materials science, paving the way toward simplified integration into a unified, consolidated data space of high value. KW - Semantic Interoperability KW - Data Spaces KW - Integration Workflows KW - MaterialDigital PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-622952 DO - https://doi.org/10.1002/adem.202401092 SN - 1527-2648 SP - 1 EP - 25 PB - Wiley-VCH CY - Weinheim AN - OPUS4-62295 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mendive Tapia, Eduardo A1 - Patrick, Christopher E A1 - Hickel, Tilmann A1 - Neugebauer, Jörg A1 - Staunton, Julie B T1 - Quantification of electronic and magnetoelastic mechanisms of first-order magnetic phase transitions from first principles: application to caloric effects in La(FexSi1-x)(13) N2 - La(FexSi1−x)13 and derived quaternary compounds are well-known for their giant, tunable, magneto- and barocaloric responses around a first-order paramagnetic-ferromagnetic transition near room temperature with low hysteresis. Remarkably, such a transition shows a large spontaneous volume change together with itinerant electron metamagnetic features. While magnetovolume effects are well-established mechanisms driving first-order transitions, purely electronic sources have a long, subtle history and remain poorly understood. Here we apply a disordered local moment picture to quantify electronic and magnetoelastic effects at finite temperature in La(FexSi1−x)13 from first-principles. We obtain results in very good agreement with experiment and demonstrate that the magnetoelastic coupling, rather than purely electronic mechanisms, drives the first-order character and causes at the same time a huge electronic entropy contribution to the caloric response. KW - Caloric effects KW - Ab initio thermodynamics KW - Magnetoeleastic couplin PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-587882 DO - https://doi.org/10.1088/2515-7655/acd027 SN - 2515-7655 VL - 5 IS - 3 SP - 1 EP - 16 PB - IOP Publishing AN - OPUS4-58788 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Tehranchi, Ali A1 - Chakraborty, Poulami A1 - López Freixes, Martí A1 - McEniry, Eunan J. A1 - Gault, Baptiste A1 - Hickel, Tilmann A1 - Neugebauer, Jörg T1 - Tailoring negative pressure by crystal defects: Microcrack induced hydride formation in Al alloys N2 - Climate change motivates the search for non-carbon-emitting energy generation and storage solutions. Metal hydrides show promising characteristics for this purpose. They can be further stabilized by tailoring the negative pressure of microstructural and structural defects. Using systematic ab initio and atomistic simulations, we demonstrate that an enhancement in the formation of hydrides at the negatively pressurized tip region of the microcrack is feasible by increasing the mechanical tensile load on the specimen. The theoretical predictions have been used to reassess and interpret atom probe tomography experiments for a high-strength 7XXX-aluminium alloy that show a substantial enhancement of hydrogen concentration at structural defects near a stress-corrosion crack tip. These results contain important implications for enhancing the capability of metals as H-storage materials. KW - Physics and Astronomy (miscellaneous) KW - Hydrogen storage KW - Ab initio Simulation KW - Microcracks PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-587878 DO - https://doi.org/10.1103/PhysRevMaterials.7.105401 SN - 2475-9953 VL - 7 IS - 10 SP - 105401-1 EP - 105401-12 PB - American Physical Society (APS) AN - OPUS4-58787 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Janssen, Jan A1 - George, Janine A1 - Geiger, Julian A1 - Bercx, Marnik A1 - Wang, Xing A1 - Ertural, Christina A1 - Schaarschmidt, Jörg A1 - Ganose, Alexander Miguel A1 - Pizzi, Giovanni A1 - Hickel, Tilmann A1 - Neugebauer, Jörg 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 - Worklows KW - FAIR Workflows KW - Automation KW - Materials Acceleration Platforms PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-643325 DO - https://doi.org/10.1039/D5DD00231A SN - 2635-098X SP - 1 EP - 14 PB - Royal Society of Chemistry (RSC) AN - OPUS4-64332 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -