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The LOBSTER (Deringer et al., 2011;Maintz et al., 2013 ,2016 ;Nelson et al., 2020 ) software aids in extracting quantum-chemical bonding information from materials by projecting the plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. LobsterEnv, a module implemented in pymatgen (Ong et al., 2013) by some of the authors of this package, facilitates the use of quantum-chemical bonding information obtained from LOBSTER calculations to identify neighbors and coordination environments. LobsterPy is a Python package that offers a set of convenient tools to further analyze and summarize the LobsterEnv outputs in the form of JSONs that are easy to interpret and process. These tools enable the estimation of (anti) bonding contributions, generation of textual descriptions, and visualization of LOBSTER computation results. Since its first release, both LobsterPy and LobsterEnv capabilities have been extended significantly. Unlike earlier versions, which could only automatically analyze Crystal Orbital Hamilton Populations (COHPs) (Dronskowski & Blöchl, 1993), both can now also analyze Crystal Orbital Overlap Populations (COOP) (Hughbanks & Hoffmann, 1983) and Crystal Orbital Bond Index (COBI) (Müller et al., 2021). Extracting the information about the most important orbitals contributing to the bonds is optional, and users can enable it as needed. Additionally, bonding-based features for machinelearning (ML) studies can be engineered via the sub-packages “featurize” and “structuregraphs”. Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. LobsterPy has been used to produce the results in Ngo et al. (2023), Chen et al. (2024), Naik et al. (2023), and it is also part of Atomate2 (2023) bonding analysis workflow for generating bonding analysis data in a format compatible with the Materials Project (Jain et al., 2013) API.
A large amount of publicly available data is reproduced every day in the field of materials science, while these kind of material data can have different formats and types like paper-type publications, standards, datasheets or isolated datasets in repositories. However, gathering a specific library from such extensive and diverse material data is always challenging for the materials scientists and engineers, since the time-related limitations are not allowed to fully access the large publicly available databases; search across these disparate databases, manage the large volumes of heterogeneous datasets, and integrate data from multiple sources. To address these challenges and make data findable, accessible, interoperable, and reusable (FAIR), an efficient data management system is necessary to build comprehensive, documented, and connected data spaces in the future. A formal standardized knowledge representation through an ontology can address such problems and make data more available and interoperable between related domains. Ontology can also rich machine processable semantic descriptions that increases the performance of scientific searches. In this regard, the Platform MaterialDigital (PMD) is currently working on developing a high-level ontology for the materials and material related processes. For example, in one of the PMD projects of “KupferDigital”, we will try to develop a data ecosystem for digital materials research based on ontology-based digital representations of copper and copper alloys. As a case study, this paper describes the methodology for ontology development of Brinell hardness, based on PMD core ontology. The methodology we describe includes the following steps; gathering the required domain terminology from different resources like standards (DIN EN ISO 6506-1) and test reports, representing the performance of a standard-conformant hardness test and the treatment of the recorded values up to a “reportable” hardness value for a material, designing the process chain according to the semantic technologies, and developing a domain-level ontology of Brinell hardness based on PMD ontology semantic formalization. Apart the mentioned methodology, some interesting tools and methods were introduced and ontology design challenges and possible solutions for modelling materials and processes were discussed. Furthermore, a dataset from the Brinell hardness measurement of cast copper samples is prepared for testing the query process.
In diesem Vortrag wird am Beispiel magnetischer Werkstoffe zur Energiewandlung gezeigt, wie röntgentomographische Untersuchungen zur Strukturaufklärung in Kompositen und Massivproben beitragen können. Die Bauteile werden zerstörungsfrei geprüft, um Risse, Poren und andere Defekte und ihren Einfluss auf die funktionellen Eigenschaften dreidimensional und rechtzeitig im Lebenszyklus des Werkstoffs zu charakterisieren. Kombiniert man Mikrotomographie mit anderen Methoden der magnetischen Werkstoffcharakterisierung, lassen sich einzigartige Aussagen über den Aufbau und die funktionellen Eigenschaften treffen.
To date, there are only very few technologies available for the conversion of low temperature waste heat to electricity. In this talk, we first describe the principle of thermomagnetic generators. Then we focus on the impact of topology of the magnetic circuit within thermomagnetic generators. We demonstrate that the key operational parameters strongly depend on the genus, i.e. the number of holes within the magnetic circuit.
Using magnetic materials for energy conversion as an example, this lecture shows how X-ray tomography investigations can contribute to structure elucidation in composites and solid samples. The components are tested non-destructively in order to characterize cracks, pores and other defects and their influence on the functional properties three-dimensionally and in good time in the life cycle of the material. If you combine microtomography with other methods of magnetic material characterization, you can make unique statements about the structure and the functional properties.