arXiv subjects

Ajay Annamareddy

Publications and source records attributed to Ajay Annamareddy.

10 recordsLinked to original sources

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

Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentation

Analyzing microstructural defects in transmission electron microscopy (TEM) images, particularly in irradiated metal alloys, is often limited by the availability of high-quality, labeled data. To address this, we introduce a generative data augmentation approach using a mask-conditioned latent diffusion model (LDM) for synthesizing realistic TEM images with controllable, automatically labeled multi-class defect masks. Without requiring manual annotations for generation, our method enables the creation of synthetic image-mask pairs by sampling distributions learned from experimental masks. These generated data were used to augment small experimental datasets of varying sizes (10, 50, and 100 labeled experimental images) to train a Mask Regional Convolutional Neural Network (R-CNN) model for defect detection and classification. Our results show that generative augmentation yields small overall model performance improvements, with up to a 0.02 gain in the harmonic mean of detection and classification F1 scores. However, we also find that the relative contributions to detection and classification improvement depend on the specific train/test data split. These findings highlight the potential of targeted generative models to enhance deep learning performance in data-scarce microscopy-based image quantification tasks.

cs.CV↗

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↗

arXiv2025

Physical regularized Hierarchical Generative Model for Metallic Glass Structural Generation and Energy Prediction

Disordered materials such as glasses, unlike crystals, lack long range atomic order and have no periodic unit cells, yielding a high dimensional configuration space with widely varying properties. The complexity not only increases computational costs for atomistic simulations but also makes it difficult for generative AI models to deliver accurate property predictions and realistic structure generation. In this work, we introduce GlassVAE, a hierarchical graph variational autoencoder that uses graph representations to learn compact, rotation, translation, and permutation invariant embeddings of atomic configurations. The resulting structured latent space not only enables efficient generation of novel, physically plausible structures but also supports exploration of the glass energy landscape. To enforce structural realism and physical fidelity, we augment GlassVAE with two physics informed regularizers, a radial distribution function (RDF) loss that captures characteristic short and medium range ordering and an energy regression loss that reflects the broad configurational energetics. Both theoretical analysis and experimental results highlight the critical impact of these regularizers. By encoding high dimensional atomistic data into a compact latent vector and decoding it into structures with accurate energy predictions, GlassVAE provides a fast, physics aware path for modeling and designing disordered materials.

cs.CE↗

arXiv2023

How close are the classical two-body potentials to ab initio calculations? Insights from linear machine learning based force matching

In this work, we propose a linear machine learning force matching approach that can directly extract pair atomic interactions from ab initio calculations in amorphous structures. The local feature representation is specifically chosen to make the linear weights a force field as a force/potential function of the atom pair distance. Consequently, this set of functions is the closest representation of the ab initio forces given the two-body approximation and finite scanning in the configurational space. We validate this approach in amorphous silica. Potentials in the new force field (consisting of tabulated Si-Si, Si-O, and O-O potentials) are significantly softer than existing potentials that are commonly used for silica, even though all of them produce the tetrahedral network structure and roughly similar glass properties. This suggests that those commonly used classical force fields do not offer fundamentally accurate representations of the atomic interaction in silica. The new force field furthermore produces a lower glass transition temperature ($T_g\sim$1800 K) and a positive liquid thermal expansion coefficient, suggesting the extraordinarily high $T_g$ and negative liquid thermal expansion of simulated silica could be artifacts of previously developed classical potentials. Overall, the proposed approach provides a fundamental yet intuitive way to evaluate two-body potentials against ab initio calculations, thereby offering an efficient way to guide the development of classical force fields.

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

Compositional Trends in Surface Enhanced Diffusion in Lead Silicate Glasses

In this work, we use molecular dynamics simulations to study the enhancement of surface over bulk diffusion (surface enhanced diffusion) in (PbO)x(SiO2)1-x glasses. This work is motivated to better understand surface diffusion in glasses and its connection to fragility, and to enhance surface diffusion in silica and related glasses for greater thermodynamic stability during vapor-deposition. By adding PbO to silica, the fragility of glass increases continuously for 10% <= x <= 70% during experiments. The increase in fragility may correspond to an increase in surface enhanced diffusion, as fragility and surface diffusion are correlated. We observe that for the silicates investigated, while surface enhanced diffusion increases with fragility, the enhancement is quite small. The slower diffusing Si and O atoms have higher enhancements, which could allow for some surface stabilization effects. We demonstrate that there are only small changes in atomic arrangements, consistent with the similar diffusion rates, at the surface as compared to bulk. Finally, we examine the trend of bulk versus surface diffusion in view of previous observations in organic and metallic glasses and found that in oxides, fragility increase may not be strongly linked to enhanced surface diffusion.

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

Factors correlating to enhanced surface diffusion in metallic glasses

The enhancement of surface diffusion (DS) over the bulk (DV) in metallic glasses (MGs) is well documented and likely to strongly influence the properties of glasses grown by vapor deposition. Here, we use classical molecular dynamics simulations to identify different factors influencing the enhancement of surface diffusion in MGs. MGs have a simple atomic structure and belong to the category of moderately fragile glasses that undergo pronounced slowdown of bulk dynamics with cooling close to the glass transition temperature (Tg). We observe that DS exhibits a much more moderate slowdown compared to DV when approaching Tg, and DS/DV at Tg varies by two orders of magnitude among the MGs investigated. We demonstrate that both the surface energy and the fraction of missing bonds for surface atoms show good correlation to DS/DV, implying that the loss of nearest neighbors at the surface directly translates into higher mobility, unlike the behavior of network- and hydrogen-bonded organic glasses. Fragility, a measure of the slowdown of bulk dynamics close to Tg, also correlates to DS/DV, with more fragile systems having larger surface enhancement of mobility. The deviations observed in the fragility and DS over DV relationship are shown to be correlated to the extent of segregation or depletion of the mobile element at the surface. Finally, we explore the relationship between the diffusion pre-exponential factor (D0) and activation energy (Q) and compare to a ln(D0)-Q correlation previously established for bulk glasses, demonstrating similar correlations from MD as in the experiments and that the surface and bulk have very similar ln(D0)-Q correlations.

cond-mat.mtrl-sci↗