arXiv subjects

Austin C. Houston

Publications and source records attributed to Austin C. Houston.

3 recordsLinked to original sources

arXiv2026

Ultralight High-Entropy Nanowire Scaffolds for Extreme-Temperature Functionality

High-entropy alloys (HEAs) combine compositional disorder with exceptional functional tunability, yet their inherently high-density limits use in lightweight systems. Here, we introduce entropy-architected nanowire metamaterials, a class of materials that couple configurational entropy with structural porosity to achieve metal-like functionality at ultralow density. FeCoNiCrCu HEA nanowires were electrodeposited into porous templates and freeze-cast into three-dimensional ``bird`s-nest`` scaffolds with densities below 1 $\%$ of the bulk metal. The resulting architectures retain a disordered face-centered-cubic phase, exhibit Curie temperatures exceeding 1000 K, and deliver thermal diffusivity ($\approx0.211$ mm$^2$ s$^{-1}$) comparable to titanium alloys. Structural and spectroscopic analyses reveal nanoscale Cu segregation that enhances magnetic ordering and thermal stability. These findings demonstrate that configurational entropy and architectural hierarchy can be co-engineered to yield lightweight, high-temperature functional materials for extreme-environment applications.

cond-mat.mtrl-sci↗

arXiv2025

Atom identification in bilayer moire materials with Gomb-Net

Moire patterns in van der Waals bilayer materials complicate the analysis of atomic-resolution images, hindering the atomic-scale insight typically attainable with scanning transmission electron microscopy. Here, we report a method to detect the positions and identities of atoms in each of the individual layers that compose twisted bilayer heterostructures. We developed a deep learning model, Gomb-Net, which identifies the coordinates and atomic species in each layer, effectively deconvoluting the moire pattern. This enables layer-specific mapping of quantities like strain and dopant distributions, unlike other commonly used segmentation models which struggle with moire-induced complexity. Using this approach, we explored the Se atom substitutional site distribution in a twisted fractional Janus WS2-WS2(1-x)Se2x heterostructure and found that layer-specific implantation sites are unaffected by the moire pattern's local energetic or electronic modulation. This advancement enables atom identification within material regimes where it was not possible before, opening new insights into previously inaccessible material physics.

cond-mat.mtrl-sci↗

arXiv2024

Unsupervised Reward-Driven Image Segmentation in Automated Scanning Transmission Electron Microscopy Experiments

Automated experiments in scanning transmission electron microscopy (STEM) require rapid image segmentation to optimize data representation for human interpretation, decision-making, site-selective spectroscopies, and atomic manipulation. Currently, segmentation tasks are typically performed using supervised machine learning methods, which require human-labeled data and are sensitive to out-of-distribution drift effects caused by changes in resolution, sampling, or beam shape. Here, we operationalize and benchmark a recently proposed reward-driven optimization workflow for on-the fly image analysis in STEM. This unsupervised approach is much more robust, as it does not rely on human labels and is fully explainable. The explanatory feedback can help the human to verify the decision making and potentially tune the model by selecting the position along the Pareto frontier of reward functions. We establish the timing and effectiveness of this method, demonstrating its capability for real-time performance in high-throughput and dynamic automated STEM experiments. The reward driven approach allows to construct explainable robust analysis workflows and can be generalized to a broad range of image analysis tasks in electron and scanning probe microscopy and chemical imaging.

cond-mat.mtrl-sci↗