arXiv subjects

Alaukik Saxena

Publications and source records attributed to Alaukik Saxena.

7 recordsLinked to original sources

arXiv2025

Identifying grain boundary and intragranular pinning centres in Sm2(Co,Fe,Cu,Zr)17 permanent magnets to guide performance optimisation

Permanent magnets draw their properties from a complex interplay of chemical composition and phase, each with their associated intrinsic magnetic properties. Gaining an understanding of these interactions is the key to deciphering the origins of a permanent magnets' magnetic performance and facilitate the engineering of much improved-performing magnets. Here, we use advanced multiscale microscopy and microanalysis on a bulk Sm2(Co,Fe,Cu,Zr)17 pinning-type high-performance magnet with outstanding thermal and chemical stability. Comparison of the microstructure in regions of different composition, we demonstrate that the pinning of magnetic domains, imaged by nanoscale magnetic induction mapping, is controlled by the composition and atomic arrangement of copper. This is confirmed by micromagnetic simulations. Contrary to the belief that grain boundaries are "weak links" in magnetic materials, we demonstrate grain boundaries undergo magnetization reversal at relatively low fields (0.1-0.3 T), but this remains confined to these regions and does not significantly impact the magnet's coercivity. Our results showcase that it is the optimal microstructure within the grain itself that is crucial for achieving the desired magnetic properties.

cond-mat.mtrl-sci↗

arXiv2025

Direct observation of nanoscale pinning centers in Ce(Co0.8Cu0.2)5.4 permanent magnets

Permanent magnets containing rare earth elements are essential components for the electrification of society. Ce(Co1-xCux)5 permanent magnets are a model system known for their substantial coercivity, yet the underlying mechanism remains unclear. Here, we investigate Ce(Co0.8Cu0.2)5.4 magnets with a coercivity of ~1 T. Using transmission electron microscopy (TEM) and atom probe tomography (APT), we identify a nanoscale cellular structure formed by spinodal decomposition. Cu-poor cylindrical cells (~5-10 nm in diameter, ~20 nm long) have a disordered CeCo5-type structure and a composition Ce(Co0.9Cu0.1)5.3. Cu-rich cell boundaries are ~ 5 nm thick and exhibit a modified CeCo5 structure, with Cu ordered on the Co sites and a composition Ce(Co0.7Cu0.3)5.0. Micromagnetic simulations demonstrate that the intrinsic Cu concentration gradients up to 12 at.% Cu/nm lead to a spatial variation in magnetocrystalline anisotropy and domain wall energy, resulting in effective pinning and high coercivity. Compared to Sm2Co17-type magnets, Ce(Co0.8Cu0.2)5.4 displays a finer-scale variation of conventional pinning with lower structural and chemical contrast in its underlying nanostructure. The identification of nanoscale chemical segregation in nearly single-phase Ce(Co0.8Cu0.2)5.4 magnets provides a microstructural basis for the long-standing phenomenon of "giant intrinsic magnetic hardness" in systems such as SmCo5-xMx, highlighting avenues for designing rare-earth-lean permanent magnets via controlled nanoscale segregation.

cond-mat.mtrl-sci↗

arXiv2025

Machine learning enhanced atom probe tomography analysis: a snapshot review

Atom probe tomography (APT) is a burgeoning characterization technique that provides compositional mapping of materials in three-dimensions at near-atomic scale. Since its significant expansion in the past 30 years, we estimate that one million APT datasets have been collected, each containing millions to billions of individual ions. Their analysis and the extraction of microstructural information has largely relied upon individual users whose varied level of expertise causes clear and documented bias. Current practices hinder efficient data processing, and make challenging standardization and the deployment of data analysis workflows that would be compliant with FAIR data principles. Over the past decade, building upon the long-standing expertise of the APT community in the development of advanced data processing or data mining techniques, there has been a surge of novel machine learning (ML) approaches aiming for user-independence, and that are efficient, reproducible, and robust from a statistics perspective. Here, we provide a snapshot review of this rapidly evolving field. We begin with a brief introduction to APT and the nature of the APT data. This is followed by an overview of relevant ML algorithms and a comprehensive review of their applications to APT. We also discuss how ML can enable discoveries beyond human capability, offering new insights into the mechanisms within materials. Finally, we provide guidance for future directions in this domain.

cond-mat.mtrl-sci↗

arXiv2024

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.

cond-mat.mtrl-sci↗

arXiv2023

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 °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.

cond-mat.mtrl-sci↗

arXiv2023

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.

physics.chem-ph↗

arXiv2022

On Strong-Scaling and Open-Source Tools for High-Throughput Quantification of Material Point Cloud Data: Composition Gradients, Microstructural Object Reconstruction, and Spatial Correlations

Characterizing microstructure-material-property relations calls for software tools which extract point-cloud- and continuum-scale-based representations of microstructural objects. Application examples include atom probe, electron, and computational microscopy experiments. Mapping between atomic- and continuum-scale representations of microstructural objects results often in representations which are sensitive to parameterization; however assessing this sensitivity is a tedious task in practice. Here, we show how combining methods from computational geometry, collision analyses, and graph analytics yield software tools for automated analyses of point cloud data for reconstruction of three-dimensional objects, characterization of composition profiles, and extraction of multi-parameter correlations via evaluating graph-based relations between sets of meshed objects. Implemented for point clouds with mark data, we discuss use cases in atom probe microscopy that focus on interfaces, precipitates, and coprecipitation phenomena observed in different alloys. The methods are expandable for spatio-temporal analyses of grain fragmentation, crystal growth, or precipitation.

cond-mat.mtrl-sci↗