On the role of Grain Boundary Character in the Stress Corrosion Cracking of Nanoporous Gold Thin Films

For its potential as a catalyst, nanoporous gold (NPG) prepared through dealloying of bulk Ag-Au alloys has been extensively investigated. NPG thin films can offer ease of handling, better tunability of the chemistry and microstructure of the nanoporous structure, and represent a more sustainable usage of scarce resources. These films are however prone to intergranular cracking during dealloying, limiting their stability and potential applications. Here, we set out to systematically investigate the grain boundaries in Au28Ag72 thin films. We observe that a sample synthesized at 400 {\deg}C is at least 2.5 times less prone to cracking compared to a sample synthesized at room temperature. This correlates with a higher density of coincident site lattice grain boundaries, especially the density of coherent sigma 3, increased, which appear resistant against cracking. Nanoscale compositional analysis of random high-angle grain boundaries reveals prominent Ag enrichment up to 77 at.%, whereas sigma 3 coherent twin boundaries show Au enrichment of up to 30 at.%. The misorientation and the chemistry of grain boundaries hence affect their dealloying behavior, which in turn controls the cracking, and the possible longevity of NPG thin films for application in electrocatalysis

A machine learning framework for quantifying chemical segregation and microstructural features in atom probe tomography data

Atom probe tomography (APT) is ideally suited to characterize and understand the interplay of chemical segregation and microstructure in modern multicomponent materials. Yet, the quantitative analysis typically relies on human expertise to define regions of interest. We introduce a computationally efficient, multistage machine learning strategy to identify chemically distinct domains in a semi automated way, and subsequently quantify their geometric and compositional characteristics. In our algorithmic pipeline, we first coarse grain the APT data into voxels, collect the composition statistics, and decompose it via clustering in composition space. The composition classification then enables the real space segmentation via a density based clustering algorithm, thus revealing the microstructure at voxel resolution. Our approach is demonstrated for a Sm(Co,Fe)ZrCu alloy. The alloy exhibits two precipitate phases with a plate-like, but intertwined morphology. The primary segmentation is further refined to disentangle these geometrically complex precipitates into individual plate like parts by an unsupervised approach based on principle component analysis, or a U-Net based semantic segmentation trained on the former. Following the chemical and geometric analysis, detailed chemical distribution and segregation effects relative to the predominant plate-like geometry can be readily mapped without resorting to the initial voxelization

Roadmap on data-centric materials science

Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research

Simulation of the θ′ Precipitation Process with Interfacial Anisotropy Effects in Al-Cu Alloys

The effects of anisotropic interfacial properties and heterogeneous elasticity on the growth and ripening of plate-like θ′-phase (Al2Cu) in Al-1.69 at.% Cu alloy are studied. Multi-phase-field simulations are conducted and discussed in comparison with aging experiments. The precipitate/matrix interface is considered to be anisotropic in terms of its energy and mobility. We find that the additional incorporation of an anisotropic interfacial mobility in conjunction with the elastic anisotropy result in substantially larger aspect ratios of the precipitates closer to the experimental observations. The anisotropy of the interfacial energy shows comparably small effect on the precipitate’s aspect ratio but changes the interface’s shape at the rim. The effect of the chemo-mechanical coupling, i.e., the composition dependence of the elastic constants, is studied as well. We show that the inverse ripening phenomenon, recently evidenced for δ’ precipitates in Al-Li alloys (Park et al. Sci. Rep. 2019, 9, 3981), does not establish for the θ′ precipitates. This is because of the anisotropic stress fields built around the θ′ precipitates, stemming from the precipitate’s shape and the interaction among different variants of the θ′ precipitate, that disturb the chemo-mechanical effects. These results show that the chemo-mechanical effects on the precipitation ripening strongly depend on the degree of sphericity and elastic isotropy of the precipitate and matrix phases.DFG, 237105621, SPP 1713: Stark gekoppelte thermo-chemische und thermo-mechanische Zustände in Angewandten Materialie

Roadmap on Data-Centric Materials Science

Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research