Andrea M. Hodge | Searcharxiv

arXiv2026

Phase nucleation, coarsening and evolution pathways of a sputtered nanostructured Inconel 725 alloy during heat treatment

Physical vapor deposition enables the fabrication of nanostructured superalloys with unique defect architectures, yet their phase evolution pathways can differ significantly from those of conventionally processed alloys. In this study, the effects of solution and aging treatments on phase selection and precipitation behavior in sputtered Inconel 725 films with an initially uniform columnar nanotwinned structure were systematically investigated. Direct aging at relatively low temperatures promoted extensive δ-phase precipitation at twin boundaries and defect-rich regions, which depleted Nb from the γ matrix and suppressed γ'/γ" precipitation. In contrast, high-temperature solution treatment induced recrystallization and eliminated the nanotwinned structure, significantly reducing δ-phase precipitation and increasing Nb availability to enable the formation of ultrafine spherical γ'/γ" precipitates within a refined γ matrix (<1 μm). Subsequent aging treatments promoted elemental partitioning and drove the morphological evolution of γ'/γ" precipitates from spherical to lenticular forms, while δ precipitation became increasingly concentrated along grain boundaries. This spatial separation of intragranular γ'/γ" and grain-boundary δ phases enabled simultaneous precipitation strengthening and grain stabilization, resulting in hardness values approaching 9 GPa. As a whole, this study demonstrates that the initial templates provided by defect structures can govern phase selection and precipitation pathways, providing a strategy for tailoring microstructure and achieving synergistic strengthening in nanostructured superalloys.

cond-mat.mtrl-sci↗

arXiv2025

opXRD: Open Experimental Powder X-ray Diffraction Database

Powder X-ray diffraction (pXRD) experiments are a cornerstone for materials structure characterization. Despite their widespread application, analyzing pXRD diffractograms still presents a significant challenge to automation and a bottleneck in high-throughput discovery in self-driving labs. Machine learning promises to resolve this bottleneck by enabling automated powder diffraction analysis. A notable difficulty in applying machine learning to this domain is the lack of sufficiently sized experimental datasets, which has constrained researchers to train primarily on simulated data. However, models trained on simulated pXRD patterns showed limited generalization to experimental patterns, particularly for low-quality experimental patterns with high noise levels and elevated backgrounds. With the Open Experimental Powder X-Ray Diffraction Database (opXRD), we provide an openly available and easily accessible dataset of labeled and unlabeled experimental powder diffractograms. Labeled opXRD data can be used to evaluate the performance of models on experimental data and unlabeled opXRD data can help improve the performance of models on experimental data, e.g. through transfer learning methods. We collected 92552 diffractograms, 2179 of them labeled, from a wide spectrum of materials classes. We hope this ongoing effort can guide machine learning research toward fully automated analysis of pXRD data and thus enable future self-driving materials labs.

cond-mat.mtrl-sci↗