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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.
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.
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.
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.