arXiv subjects

Paul M. Voyles

Publications and source records attributed to Paul M. Voyles.

At least 19 recordsLinked to original sources

arXiv2026

Remote epitaxial frustration stabilizes a correlated interfacial state

Remote epitaxy exploits substrate interactions transmitted across atomically thin materials to replicate substrate crystal structure. Here we show that competition among graphene-, substrate-, and reconstruction-derived interactions can instead produce frustration. Using GdAuGe films on $N$-layer graphene/SiC(0001), we identify at intermediate $N$ a self-limited interfacial state with broken long-range translational order, accompanied by non-monotonic crystallographic orientation selection in the epitaxial film above. The frustrated interface is accompanied by strongly enhanced magnetic irreversibility above 300 K, with an interface-dominated rather than volume-scaled response, linking epitaxial frustration to an emergent collective property. Annealing drives an initially epitaxial crystal into the frustrated state, distinguishing it from kinetically trapped disorder. First-principles calculations reveal a multi-periodic interfacial potential that provides a microscopic basis for frustration. Together, these results establish epitaxial frustration as a materials-design principle for stabilizing correlated interfacial states and emergent collective properties.

cond-mat.mtrl-sci↗

arXiv2026

Morphology and Dynamics of Self-interstitial Clusters in Irradiated Nickel

Self-interstitial atom (SIA) clustering is a key early step in radiation damage evolution in face-centered cubic (FCC) metals, governing defect transport, recombination, and the long-term microstructural response of irradiated alloys. We combine molecular dynamics (MD) simulations and high-speed (>1000 frames/s) in situ transmission electron microscopy (TEM) to investigate the structure, energetics, and migration dynamics of SIA clusters in FCC Ni. MD simulations show that interstitials initially form disordered dumbbell clusters that evolve into either sessile Frank loops or glissile perfect (prismatic) loops; the latter progressively reorganize into compact ordered configurations with increasing mobility. Direct construction of both loop types over a wide size range, validated against MD-relaxed structures, shows that perfect loops are thermodynamically favored over Frank loops for cluster sizes N greater than or equal to 14, where N is the number of SIAs, with the energetic advantage increasing with cluster size. Nevertheless, substantial kinetic barriers allow Frank loops to persist as metastable defects. For perfect loops, diffusion coefficients computed over N = 16 to 400 reveal a nearly size-independent migration barrier of approximately 0.02 eV, while the diffusion prefactor decreases approximately as N^(-0.54). Trajectory analysis reveals a non-rigid, row-wise relay mechanism in which the number of participating atoms increases systematically with loop size, accounting for much of the observed prefactor scaling. Sub-millisecond in situ TEM observations reveal intermittent loop motion at velocities higher than previously observed but still several orders of magnitude below the intrinsic mobilities predicted by MD, indicating migration through a heterogeneous energy landscape of mobile and pinned states.

cond-mat.mtrl-sci↗

arXiv2026

Accurate Evaluation of Nanoscale Spatiotemporal Dynamics with Electron Correlation Microscopy

Electron correlation microscopy (ECM) can measure materials dynamics with nanoscale spatial resolution from intensity correlation functions. However, adopting X-ray photon correlation spectroscopy (XPCS) normalization frameworks unchanged when calculating intensity correlations can introduce errors. Due to the constrained sampling volumes and larger speckle sizes in nanobeam electron diffraction, XPCS-style time-averaging and scattering-vector averaging introduce systematic artifacts, such as artificial anticorrelations or elevated baselines that lead to systematic errors in structural relaxation times and stretching exponents. This work presents physics-inspired, ECM-specific intensity normalizations over time- and azimuthal-averaged intensities of the first diffraction ring that limit those errors. The framework is validated using molecular dynamics simulations of a CuZr supercooled liquid to benchmark against the self intermediate scattering function, successfully reproducing relaxation times. When applied to experimental time-resolved 4D STEM datasets of a Pt57.5Cu14.7Ni5.3P22.5 nanowire, the method correctly identifies highly stable, unchanging nanoscale crystalline phases that were erroneously misclassified as relaxing domains by previous frameworks. Other previous ECM research is reevaluated in light of these observation. This robust approach establishes an artifact-free pathway for evaluating localized spatiotemporal relaxation behaviors.

cond-mat.mtrl-sci↗

arXiv2026

Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization

We have developed a machine learning model for critical cooling rates for metallic glasses based on computational properties. We compare results for features derived from easy-to-compute functions of elemental properties to more complex physically motivated properties using ab initio, machine-learning potentials, and empirical potential molecular dynamics methods. The established approach enables property acquisition across a diverse range of alloys. Analysis of various features for 34 alloys from 20 chemical systems shows that the best model for critical cooling rates was learned from one elemental property-based feature and three simulated features. The elemental property-based feature is an ideal entropy value based on alloy stoichiometry. The simulated features were acquired from estimates of energies above the convex hull, changes in heat capacity, and the fraction of icosahedra-like Voronoi polyhedra. Models were assessed through a demanding cross validation test based on repeatedly leaving out full chemical systems as test sets and had an $R^2$ of 0.78 and a mean average error of 0.76 in units of $[log_{10}(K/s)]$. We demonstrate with Shapley additive explanation analysis that the most impactful features have physically reasonable influence on model predictions. The established methodology can be applied to other high-throughput studies of material properties of diverse compositions.

cond-mat.mtrl-sci↗

arXiv2026

Vapor-to-glass preparation of biaxially aligned organic semiconductors

Physical vapor deposition (PVD) provides a route to prepare highly stable and anisotropic organic glasses that are utilized in multi-layer structures such as organic light-emitting devices. While previous work has demonstrated that anisotropic glasses with uniaxial symmetry can be prepared by PVD, here, we prepare biaxially aligned glasses in which molecular orientation has a preferred in-plane direction. With the collective effect of the surface equilibration mechanism and template growth on an aligned substrate, macroscopic biaxial alignment is achieved in depositions as much as 180 K below the clearing point $T_{LC-iso}$ (and 50 K below the glass transition temperature $T_g$ ) with single-component disk-like (phenanthroperylene ester) and rod-like (itraconazole) mesogens. The preparation of biaxially aligned organic semiconductors adds a new dimension of structural control for vapor-deposited glasses and may enable polarized emission and in-plane control of charge mobility.

cond-mat.mtrl-sci↗

arXiv2026

Asymmetric Energy Landscapes Control Diffusion in Glasses

While diffusion in crystalline solids is quantitatively understood through defect-mediated atomic hops, no comparable quantitative framework exists for glasses. In these systems, the origin of large diffusion activation energies remains puzzling, despite local rearrangements involving low barriers. Using molecular dynamics simulations of metallic glasses, we decompose diffusion into random-walk and correlation contributions and find that back-and-forth correlated motion, not local rearrangement barriers, dominates the activation energy, resolving how low-barrier rearrangements yield large macroscopic activation energies. These correlations arise from asymmetry between forward and reverse barriers, a generic feature of disordered energy landscapes. We find that the correlation-driven mechanism is active beyond metallic glass alloys, including SiO2 and a single-component Lennard-Jones glass. The latter demonstrates that the correlation originates from structural disorder rather than chemical complexity. The framework also explains accelerated surface diffusion, where reduced activation energies arise primarily from weaker correlations rather than changes in local rearrangement barriers. Our results establish a direct, quantitative link between atomic-scale dynamics and macroscopic transport, providing a predictive basis for kinetics in disordered materials.

cond-mat.mtrl-sci↗

arXiv2026

Epitaxial stabilization of magnetic GdAuSb/LaAuSb superlattices

We report the epitaxial stabilization of GdAuSb films and GdAuSb/LaAuSb superlattices via molecular beam epitaxy on (0001)-oriented Al$_{2}$O$_{3}$ substrates. GdAuSb crystallize in the Au-Au dimerized YPtAs structure type (space group $P6_{3}/mmc$), the same structure as the Dirac semimetal LaAuSb. Angle-resolved photoemission spectroscopy (ARPES) measurements show similar near $E_F$ bandstructures for GdAuSb and LaAuSb, plus a rigid band shift for GdAuSb towards more hole-like behavior and core-like Gd $4f$ states $\sim 9$~eV below the Fermi energy. LaAuSb/GdAuSb superlattices exhibit sharp superlattice fringes by X-ray diffraction and atomically-precise interfaces by scanning transmission electron microscopy. Superlattices display two transitions in temperature-dependent resistvity, compared to a single Néel temperature for thick GdAuSb films. Superlattices of $Ln$AuSb materials ($Ln=$ rare earth) with atomically abrupt interfaces offer a new epitaxial platform for control of magnetic and topological order via tunable intralayer exchange and reduced dimensionality.

cond-mat.mtrl-sci↗

arXiv2025

Growth and Structure of alpha-Ta films for Quantum Circuit Integration

Tantalum films incorporated into superconducting circuits have exhibited low surface losses, resulting in long-lived qubit states. Remaining loss pathways originate in microscopic defects which manifest as two level systems (TLS) at low temperature. These defects limit performance, so careful attention to tantalum film structures is critical for optimal use in quantum devices. In this work, we investigate the growth of tantalum using magnetron sputtering on sapphire, Si, and photoresist substrates. In the case of sapphire, we present procedures for growth of fully-oriented films with alpha-Ta [1 1 1] // Al2O3 [0 0 0 1] and alpha-Ta [1 -1 0] // Al2O3 [1 0 -1 0] orientational relationships, and having residual resistivity ratios (RRR) ~ 60 for 220 nm thick films. On Si, we find a complex grain texturing with Ta [1 1 0] normal to the substrate and RRR ~ 30. We further demonstrate airbridge fabrication using Nb to nucleate alpha-Ta on photoresist surfaces. For the films on sapphire, resonators show TLS-limited quality factors of 1.3 +/- 0.3 x 10^6 at 10 mK (for waveguide gap and conductor widths of 3 microns and 6 microns, respectively). Structural characterization using scanning electron microscopy, X-ray diffraction, low temperature transport, secondary ion mass spectrometry, and transmission electron microscopy reveal the dependence of residual impurities and screw dislocation density on processing conditions. The results provide practical insights for fabrication of advanced superconducting devices including qubit arrays, and guide future work on crystallographically deterministic qubit fabrication.

cond-mat.supr-con↗

arXiv2024

Tunable polar distortions and magnetism in Gd$_x$La$_{1-x}$PtSb epitaxial films

Hexagonal $ABC$ intermetallics are predicted to have tunable ferroelectric, topological, and magnetic properties as a function of the polar buckling of $BC$ atomic planes. We report the impact of isovalent lanthanide substitution on the buckling, structural phase transitions, and electronic and magnetic properties of Gd$_x$La$_{1-x}$PtSb films grown by molecular beam epitaxy (MBE) on c-plane sapphire substrates. The Gd$_x$La$_{1-x}$PtSb films form a solid solution from x = 0 to 1 and retain the polar hexagonal structure ($P6_3 mc$) out to $x \leq 0.95$. With increasing $x$, the PtSb buckling increases and the out of plane lattice constant $c$ decreases due to the lanthanide contraction. While hexagonal LaPtSb is a highly conductive polar metal, the carrier density decreases with $x$ until an abrupt phase transition to a zero band overlap semimetal is found for cubic GdPtSb at $x=1$. The magnetic susceptibility peaks at small but finite $x$, which we attribute to Ruderman Kittel Kasuya Yosida (RKKY) coupling between localized $4f$ moments, whose concentration increases with $x$, and free carriers that decrease with $x$. Samples with $x\geq 0.3$ show antiferromagnetic Curie-Weiss behavior and a Neel temperature that increases with $x$. The Gd$_x$La$_{1-x}$PtSb system provides opportunities to dramatically alter the polar buckling and concentration of local $4f$ moments, for tuning chiral spin textures and topological phases.

cond-mat.mtrl-sci↗

arXiv2024

Machine Learning Materials Properties with Accurate Predictions, Uncertainty Estimates, Domain Guidance, and Persistent Online Accessibility

One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision requires both providing detailed uncertainty quantification (model prediction errors and domain of applicability) and making models readily usable. At present, it is common practice in the community to assess ML model performance only in terms of prediction accuracy (e.g., mean absolute error), while neglecting detailed uncertainty quantification and robust model accessibility and usability. Here, we demonstrate a practical method for realizing both uncertainty and accessibility features with a large set of models. We develop random forest ML models for 33 materials properties spanning an array of data sources (computational and experimental) and property types (electrical, mechanical, thermodynamic, etc.). All models have calibrated ensemble error bars to quantify prediction uncertainty and domain of applicability guidance enabled by kernel-density-estimate-based feature distance measures. All data and models are publicly hosted on the Garden-AI infrastructure, which provides an easy-to-use, persistent interface for model dissemination that permits models to be invoked with only a few lines of Python code. We demonstrate the power of this approach by using our models to conduct a fully ML-based materials discovery exercise to search for new stable, highly active perovskite oxide catalyst materials.

cond-mat.mtrl-sci↗

arXiv2023

Room-temperature ferromagnetism in epitaxial bilayer FeSb/SrTiO3(001) terminated with a Kagome lattice

Two-dimensional (2D) magnets exhibit unique physical properties for potential applications in spintronics. To date, most 2D ferromagnets are obtained by mechanical exfoliation of bulk materials with van der Waals interlayer interactions, and the synthesis of single or few-layer 2D ferromagnets with strong interlayer coupling remains experimentally challenging. Here, we report the epitaxial growth of 2D non-van der Waals ferromagnetic bilayer FeSb on SrTiO3(001) substrates stabilized by strong coupling to the substrate, which exhibits in-plane magnetic anisotropy and a Curie temperature above 300 K. In-situ low-temperature scanning tunneling microscopy/spectroscopy and density-functional theory calculations further reveal that a Fe Kagome layer terminates the bilayer FeSb. Our results open a new avenue for further exploring emergent quantum phenomena from the interplay of ferromagnetism and topology for application in spintronics.

cond-mat.mtrl-sci↗

arXiv2023

Machine Learning Prediction of Critical Cooling Rate for Metallic Glasses From Expanded Datasets and Elemental Features

We use a random forest model to predict the critical cooling rate (RC) for glass formation of various alloys from features of their constituent elements. The random forest model was trained on a database that integrates multiple sources of direct and indirect RC data for metallic glasses to expand the directly measured RC database of less than 100 values to a training set of over 2,000 values. The model error on 5-fold cross validation is 0.66 orders of magnitude in K/s. The error on leave out one group cross validation on alloy system groups is 0.59 log units in K/s when the target alloy constituents appear more than 500 times in training data. Using this model, we make predictions for the set of compositions with melt-spun glasses in the database, and for the full set of quaternary alloys that have constituents which appear more than 500 times in training data. These predictions identify a number of potential new bulk metallic glass (BMG) systems for future study, but the model is most useful for identification of alloy systems likely to contain good glass formers, rather than detailed discovery of bulk glass composition regions within known glassy systems.

cond-mat.mtrl-sci↗

arXiv2023

Effect of Pt vacancies on magnetotransport of Weyl semimetal candidate GdPtSb epitaxial films

We examine the effects of Pt vacancies on the magnetotransport properties of Weyl semimetal candidate GdPtSb films, grown by molecular beam epitaxy on c-plane sapphire. Rutherford backscattering spectrometry (RBS) and x-ray diffraction measurements suggest that phase pure GdPt$_{x}$Sb films can accommodate up to $15\%$ Pt vacancies ($x=0.85$), which act as acceptors as measured by Hall effect. Two classes of electrical transport behavior are observed. Pt-deficient films display a metallic temperature dependent resistivity (d$ρ$/dT$>$0). The longitudinal magnetoresistance (LMR, magnetic field $\mathbf{B}$ parallel to electric field $\mathbf{E}$) is more negative than transverse magnetoresistance (TMR, $\mathbf{B} \perp \mathbf{E}$), consistent with the expected chiral anomaly for a Weyl semimetal. The combination of Pt-vacancy disorder and doping away from the expected Weyl nodes; however, suggests conductivity fluctuations may explain the negative LMR rather than chiral anomaly. Samples closer to stoichiometry display the opposite behavior: semiconductor-like resistivity (d$ρ$/dT$<$0) and more negative transverse magnetoresistance than longitudinal magnetoresistance. Hysteresis and other nonlinearities in the low field Hall effect and magnetoresistance suggest that spin disorder scattering, and possible topological Hall effect, may dominate the near stoichiometric samples. Our findings highlight the complications of transport-based identification of Weyl nodes, but point to possible topological spin textures in GdPtSb.

cond-mat.mtrl-sci↗

arXiv2023

Using 4D STEM to probe mesoscale order in molecular glass films prepared by physical vapor deposition

Physical vapor deposition can be used to prepare highly stable organic glass systems where the molecules show orientational and translational ordering at the nanoscale. We have used low-dose four-dimensional scanning transmission electron microscopy (4D STEM), enabled by a fast direct electron detector, to map columnar order in glassy samples of a discotic mesogen using a 2 nm probe. Both vapor deposited and liquid cooled glassy films show domains of similar orientation, but their size varies from tens to hundreds of nanometers, depending on processing. Domain sizes are consistent with surface diffusion mediated ordering during film deposition. These results demonstrate the ability of low-dose 4D STEM to characterize mesoscale structure in a molecular glass system which may be relevant to organic electronics.

cond-mat.mtrl-sci↗

arXiv2022

Distribution of atomic rearrangement vectors in a metallic glass

Short-timescale atomic rearrangements are fundamental to the kinetics of glasses and frequently dominated by one atom moving significantly (a rearrangement), while others relax only modestly. The rates and directions of such rearrangements (or hops) are dominated by the distributions of activation barriers (Eact) for rearrangement for a single atom and how those distributions vary across the atoms in the system. We have used molecular dynamics simulations of Cu50Zr50 metallic glass below Tg in an isoconfigurational ensemble to catalog the ensemble of rearrangements from thousands of sites. The majority of atoms are strongly caged by their neighbors, but a tiny fraction has a very high propensity for rearrangement, which leads to a power-law variation in the cage-breaking probability for the atoms in the model. In addition, atoms generally have multiple accessible rearrangement vectors, each with its own Eact. However, atoms with lower Eact (or higher rearrangement rates) generally explored fewer possible rearrangement vectors, as the low Eact path is explored far more than others. We discuss how our results influence future modeling efforts to predict the rearrangement vector of a hopping atom.

cond-mat.mtrl-sci↗

arXiv2022

Benchmark tests of atom segmentation deep learning models with a consistent dataset

The information content of atomic resolution scanning transmission electron microscopy (STEM) images can often be reduced to a handful of parameters describing each atomic column, chief amongst which is the column position. Neural networks (NNs) are a high performance, computationally efficient method to automatically locate atomic columns in images, which has led to a profusion of NN models and associated training datasets. We have developed a benchmark dataset of simulated and experimental STEM images and used it to evaluate the performance of two sets of recent NN models for atom location in STEM images. Both models exhibit high performance for images of varying quality from several different crystal lattices. However, there are important differences in performance as a function of image quality, and both models perform poorly for images outside the training data, such as interfaces with large difference in background intensity. Both the benchmark dataset and the models are available using the Foundry service for dissemination, discovery, and reuse of machine learning models.

cond-mat.mtrl-sci↗

arXiv2021

Mechanisms of bulk and surface diffusion in metallic glasses determined from molecular dynamics simulations

The bulk and surface dynamics of Cu50Zr50 metallic glass were studied using classical molecular dynamics (MD) simulations. As the alloy undergoes cooling, it passes through liquid, supercooled, and glassy states. While bulk dynamics showed a marked slowing down prior to glass formation, with increasing activation energy, the slowdown in surface dynamics was relatively subtle. The surface exhibited a lower glass transition temperature than the bulk, and the dynamics preceding the transition were accurately described by a temperature-independent activation energy. Surface dynamics were much faster than bulk at a given temperature in the supercooled state, but surface and bulk dynamics were found to be very similar when compared at their respective glass transition temperatures. The manifestation of dynamical heterogeneity, as characterized by the non-Gaussian parameter and breakdown of the Stokes-Einstein equation, was also similar between bulk and surface for temperatures scaled by their respective glass transition temperatures. Individual atom motion was dominated by a cage and jump mechanism in the glassy state for both the bulk and surface. We utilize this cage and jump mechanisms to separate the activation energy for diffusion into two parts: (i) cage-breaking barrier (Q1), associated with the rearrangement of neighboring atoms to free up space and (ii) the subsequent jump barrier (Q2). It was observed that Q1 dominates Q2 for both bulk and surface diffusion, and the difference in activation energies for bulk and surface diffusion mainly arose from the differences in cage-breaking barrier Q1.

cond-mat.mtrl-sci↗

arXiv2021

Molecular simulation-derived features for machine learning predictions of metal glass forming ability

We have developed models of metallic alloy glass forming ability based on newly computationally accessible features obtained from molecular dynamics simulations. In this work we showed that it is possible to increase the predictive value of GFA models by using input features obtained from molecular dynamics simulations. Such features require only relatively straightforward and scalable simulations, making them significantly easier and less expensive to obtain than experimental measurements. We generated a database of molecular dynamics critical cooling rates along with associated candidate features that are inspired from previous research on GFA. Out of the list of 9 proposed GFA features, we identify two as being the most important to performance through a LASSO model. Enthalpy of crystallization and icosahedral-like fraction at 100 K showed promise because they enable a significant improvement to model performance and because they are accessible to flexible ab initio quantum mechanical methods readily applicable to almost all systems. This advancement in computationally accessible features for machine learning predictions GFA will enable future models to more accurately predict new glass forming alloys.

cond-mat.mtrl-sci↗