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Al3(Sc,Zr,Ti) nanoparticles with an ideal twin-type orientation relationship to Al host matrix were found in cold-rolled and subsequently annealed Al-based alloy. Atomic-scale investigations using high-resolution scanning transmission electron microscopy identified particles that form prominent coherent (111) twin-type interfaces along their longer facets and semi-coherent twin interfaces on their shorter facets. Ab-initio calculations showed that a coherent Al/Al3Sc twin-like phase boundary corresponds to a local energy minimum. A model is proposed explaining the formation of the twin orientation relationship of an Al3Sc nanoparticle with the Al host matrix.
Since the function of materials is controlled by properties and processes on the atomic scale, ab initio based high-throughput methods are valuable strategies in materials design. For computational efficiency, they are however often restriction to T=0K calculations, while many technologically relevant materials properties and thermodynamic stabilities change when going from low to high temperatures. On the other hand, the constantly increasing performance of digital tools for simulation and data-driven science enables more targeted material development including these kinds of finite-temperature effects. A flexible infrastructure, including data management and workflow solutions is required to make this symbiosis user-friendly efficient. Within this presentation, examples from ab initio thermodynamics for the design of phase stabilities in hard-magnetic alloys and defect-phases in advanced high-strength steels will be demonstrated. We will discuss physical concepts with a focus at the impact of magnetic excitations. At the same time, the examples will be used to derive requirements and present solutions for a digital infrastructure. An outlook will be given to current strategies with the NDFI initiative NFDI-MatWerk.
The effect of spin fluctuations on the α(bcc)-γ(fcc)-δ(bcc) structural phase transitions in iron is investigated with a tight-binding (TB) model. The orthogonal d-valent TB model is combined with thermodynamic integration, spin-space averaging, and Hamiltonian Monte Carlo to compute the temperature-dependent free-energy difference between bcc and fcc iron. We demonstrate that the TB model captures experimentally observed phonon spectra of bcc iron at elevated temperatures. Our calculations show that spin fluctuations are crucial for both the α−γ and γ−δ phase transitions but they enter through different mechanisms. Spin fluctuations impact the α−γ phase transition mainly via the magnetic/electronic free-energy difference between bcc and fcc iron. The γ−δ phase transition, in contrast, is influenced by spin fluctuations only indirectly via the spin-lattice coupling. Combining the two mechanisms, we obtain both the α−γ and γ−δ phase transitions with our TB model. The calculated transition temperatures are in very good agreement with experimental values.
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.
Understanding segregation to grain boundaries, and eventually their defect phase diagrams, is critical to enable rational grain boundary engineering for alloys design. Here, we discuss the progress and challenges one may face when generating such a database of defect-solute interactions, in the context of studying of grain boundary segregation of solutes and impurities in Fe GBs. To compute the large number of relevant defect-solute interactions we have performed high-throughput ab initio calculations using efficient and highly automated workflows using pyiron. We study has been performed across a representative set of coincident-site-lattice (CSL) type tilt GBs. Based on the large ab-initio datasets, we extract and analyse the chemical and structural trends observed in the solute segregation behaviour across the periodic table. The features which are most important in evaluating site segregation are presented and discussed.
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.
Suitable material solutions are of key importance in designing and producing components for engineering systems – either for functional or structural applications. Materials data are generated, transferred, and introduced at each step along the complete life cycle of a component. A reliable materials data space is therefore crucial in the digital transformation of an industrial branch.
Therefore, the “Innovation Platform MaterialDigital (PMD) funded by the German Federal Ministry of Education and Research (BMBF), aims to develop a sustainable infrastructure for the standardized digital representation of materials science and materials engineering. With its partners (KIT, Fraunhofer IWM, FIZ, Leibnitz IWT, BAM, MPIE), the PMD is committed to build up a materials science data space. To achieve this the PMD provides a prototypical infrastructure for the digitalization of materials implemented by decentralized data servers, standardized data schemas and digital workflows. Following the FAIR principles, it will promote the semantic interoperability across the frontiers of materials classes.
Standards, methods, and tools developed within the platform are deployed and consolidated within the context of currently near 20 BMBF-funded academic and industrial research consortia and made available to the material science community in general. In this context scientific workflows represent a major focus area, represented within the platform by the workflow frameworks pyiron and SimStack. In consequence, the platform is building up a digital library in form of a workflow store along with common standards for the definition and representation of digital workflows.
In this presentation we will describe the status of our Platform MaterialDigital with a focus on the workflow activities. The current status and the vision for dissemination of the solutions developed in the PMD within the community are provided.
Material science problems have intrinsically multiscale and multiphysics characters, and require us to employ a combination of methods on different time and length scales to resolve critical features. Normally creating workflows that connect data in multiple scales and various methods is a cumbersome task. Pyiron, an integrated development environment (IDE) for material science, contains modules for the atomistic as well as continuum scale that make a seamless connection possible. To this end, it provides a high-level coherent language in a unified workflow platform to study materials, for example, with density functional theory (DFT) simulations in the same framework as with the finite element method (FEM). In this work, in addition to the introduction of different features in pyiron and its continuum module, a couple of exemplary workflows are demonstrated, bridging the scales in pyiron. One such workflow is analyzing the elastoplastic response of metallic alloys, where the elastic properties of the system are dependent on the atomistic composition and structure of the alloy. We use pyiron atomistics to model the dependence of elastic moduli on the atomistic composition of Al-Mg alloys. Benefiting from the integration of DAMASK in the continuum module of pyiron, the model can be used as an input to simulate the elastoplastic response of the alloy under various loadings. In addition, finite element simulations using the FEniCS package are currently integrated into the continuum module and will be demonstrated in the presentation.
Since the function of materials is controlled by properties and processes on the atomic scale, ab initio based high-throughput methods are valuable strategies in materials design. For computational efficiency, they are however often restriction to T=0K calculations, while many technologically relevant materials properties and thermodynamic stabilities change when going from low to high temperatures. On the other hand, the constantly increasing performance of digital tools for simulation and data-driven science enables more targeted material development including these kinds of finite-temperature effects. A flexible infrastructure, including data management and workflow solutions is required to make this symbiosis user-friendly efficient. Within this presentation, examples from ab initio thermodynamics for the design of phase stabilities in hard-magnetic alloys and defect-phases in advanced high-strength steels will be demonstrated. We will discuss physical concepts with a focus at the impact of magnetic excitations. At the same time, the examples will be used to derive requirements and present solutions for a digital infrastructure. An outlook will be given to current strategies with the NDFI initiative NFDI-MatWerk.