Hydrogen embrittlement, which severely affects structural materials such as steel, comprises several mechanisms at the atomic level. One of them is hydrogen enhanced decohesion (HEDE), the phenomenon of H accumulation between cleavage planes, where it reduces the interplanar cohesion. Grain boundaries are expected to play a significant role for HEDE, since they act as trapping sites for hydrogen. To elucidate this mechanism, we present the results of first-principles studies of the H effect on the cohesive strength of α-Fe single crystal (001) and (111) cleavage planes, as well as on the Σ5(310)[001] and Σ3(112)[1$\overline{1}$0] symmetrical tilt grain boundaries. The calculated results show that, within the studied range of concentrations, the single crystal cleavage planes are much more sensitive to a change in H concentration than the grain boundaries. Since there are two main types of procedures to perform ab initio tensile tests, different in whether or not to allow the relaxation of atomic positions, which can affect the quantitative and qualitative results, these methods are revisited to determine their effect on the predicted cohesive strength of segregated interfaces.

Hydrogen enhanced decohesion is expected to play a major role in ferritic steels, especially at grain boundaries. Here, we address the effects of some common alloying elements C, V, Cr, and Mn on the H segregation behaviour and the decohesion mechanism at a $\Sigma 5(310)\left[001\right]$ ${36.9}^{\circ }$ grain boundary in bcc Fe using spin polarized density functional theory calculations. We find that V, Cr, and Mn enhance grain boundary cohesion. Furthermore, all elements have an influence on the segregation energies of the interstitial elements as well as on these elements’ impact on grain boundary cohesion. V slightly promotes segregation of the cohesion enhancing element C. However, none of the elements increase the cohesion enhancing effect of C and reduce the detrimental effect of H on interfacial cohesion at the same time. At an interface which is co-segregated with C, H, and a substitutional element, C and H show only weak interaction, and the highest work of separation is obtained when the substitute is Mn.

Hydrogen-enhanced decohesion (HEDE) is one of the many mechanisms of hydrogen embrittlement, a phenomenon that severely impacts structural materials such as iron and iron alloys. Grain boundaries (GBs) play a critical role in this mechanism, where they can provide trapping sites or act as hydrogen diffusion pathways. The interaction of H with GBs and other crystallographic defects, and thus the solubility and distribution of H in the microstructure, depends on the concentration, chemical potential, and local stress. Therefore, for a quantitative assessment of HEDE, a generalized solution energy in conjunction with the cohesive strength as a function of hydrogen coverage is needed. In this paper, we carry out density functional theory calculations to investigate the influence of H on the decohesion of the $\mathrm{\Sigma }5\left(310\right)\left[001\right]$ and $\mathrm{\Sigma }3\left(112\right)\left[1\overline{1}0\right]$ symmetrical tilt GBs in bcc Fe, as examples for open and close-packed GB structures. A method to identify the segregation sites at the GB plane is proposed. The results indicate that at higher local concentrations, H leads to a significant reduction of the cohesive strength of the GB planes, significantly more pronounced at the $\mathrm{\Sigma }5$ than at the $\mathrm{\Sigma }3$ GB. Interestingly, at finite stress, the $\mathrm{\Sigma }3$ GB becomes more favorable for H solution, as opposed to the case of zero stress, where the $\mathrm{\Sigma }5$ GB is more attractive. This suggests that, under certain conditions, stresses in the microstructure can lead to a redistribution of H to the stronger grain boundary, which opens a path to designing H-resistant microstructures. To round up our study, we investigate the effects of typical alloying elements in ferritic steel, C, V, Cr, and Mn, on the solubility of H and the strength of the GBs.

Based on our experience within the NFDI4Culture and NFDI-MatWerk projects we propose generalized knowledge graph based research data management solutions, which are applicable to other consortia. Our solution covers the construction of a common NFDI core ontology adapted to specific domains via domain extensions as a basis for a knowledge graph (KG) providing information about a consortium and its related research data and software resources. This KG serves as a backend for the web portal that enables interactive access and management of this data. Already implemented for NFDI4Culture and to be adapted by NFDI-MatWerk, this solution might serve as an example solution also for other consortia. We are synchronizing our efforts with ongoing work to implement knowledge graph based research data management in NFDI4DataScience.

Low-nickel austenitic steel is subjected to high-pressure torsion fatigue (HPTF) loading, where a constant axial compression is overlaid with a cyclic torsion. The focus of this work lies on investigating whether isotropic J2 plasticity or crystal plasticity can describe the mechanical behavior during HPTF loading, particularly focusing on the axial creep deformation seen in the experiment. The results indicate that a J2 plasticity model with an associated flow rule fails to describe the axial creep behavior. In contrast, a micromechanical model based on an empirical crystal plasticity law with kinematic hardening described by the Ohno–Wang rule can match the HPTF experiments quite accurately. Hence, our results confirm the versatility of crystal plasticity in combination with microstructural models to describe the mechanical behavior of materials under reversing multiaxial loading situations.

The reuse of atomistic simulation data is often limited by heterogeneous formats, incomplete metadata, and a lack of standardized representations of workflows and provenance. Here we present an ontology-based infrastructure for representing and integrating atomistic simulation data as a knowledge graph. The approach combines domain ontologies with a software framework that enables data capture both from existing datasets and directly from simulation workflows at the point of generation. Heterogeneous data from multiple sources are normalized into a common, ontology-aligned representation, enabling consistent querying and analysis across datasets. We demonstrate these capabilities through the integration of grain boundary data, cross-dataset analysis of material properties, and extraction of derived thermodynamic quantities from existing simulations. In addition, workflows are represented in a machine-readable form, enabling both forward provenance tracking and partial reconstruction of computational procedures. The resulting knowledge graph contains over 750,000 triples describing nearly 8,000 computational samples. This work provides a practical framework for improving the findability, interoperability, and reuse of atomistic simulation data.