arXiv subjects

Saryu Fensin

Publications and source records attributed to Saryu Fensin.

15 recordsLinked to original sources

arXiv2026

A new Fermi kinetic phase transition model for metals including hysteresis effects

We present a new phenomenological model for phase transformation (PT) kinetics in metals, the "Fermi Kinetic Phase Transition (KPT) Model". It is designed such that it captures the main macroscopic features of our previously developed micro-structure dependent model, but at a fraction of the computational cost of the latter. Using four model parameters, the Fermi KPT model performs better than other phenomenological PT kinetics models in the literature, as shown by our present comparisons to experimental data for iron and tin.

cond-mat.mtrl-sci↗

arXiv2026

An Agentic Orchestration of Atomistic Simulations

Atomistic simulations are central to materials design, but their execution involves complex, multi-step workflows that require significant human expertise. Here, we present an agent-based system embedded within the URSA (Universal Research and Scientific Agent) framework that automates the design, execution, and validation of atomistic simulations, demonstrated using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) tool. Our system autonomously selects interatomic potentials, constructs and runs simulations, and performs iterative error recovery within a closed-loop workflow. We evaluate the scientific reliability of the agent by benchmarking its outputs against LAVA, a high-throughput toolkit for LAMMPS and the Vienna Ab initio Simulation Package (VASP) calculations. Our framework reduces manual intervention and trial-and-error, thereby improving the rigor, reproducibility, and scalability of atomistic modeling.

cs.AI↗

arXiv2026

Leveraging mechanical resonances for the selection of promising materials in complex phase spaces

The "high-entropy" paradigm has been applied to a central challenge in materials science, the design of new functional materials with enhanced performance for targeted applications, with some notable successes over the last twenty years. However, the immensity of the high-entropy design space remains a major impediment to discovering optimal compositions with tailored microstructures. Suites of high-throughput computational tools have been developed to address this problem, but there is a compelling need to inform these models with fast, economical, non-destructive, and versatile experimental guidance. In this work, we demonstrate mechanical resonance measurements can fulfill this need. Mechanical resonance measurements enable the rapid, non-destructive assessment of materials created by novel syntheses and/or processes and provide high-accuracy determinations of elastic constants to directly benchmark models. We exemplify these capabilities on W-Ta-Cr-V-Hf and Mo-Nb-Ti-V-Zr refractory high-entropy alloys and suggest methodologies for the wider adoption and application of mechanical resonance measurements.

cond-mat.mtrl-sci↗

arXiv2026

Thicker amorphous grain boundary complexions reduce plastic strain localization in nanocrystalline Cu-Zr

Amorphous grain boundary complexions have been shown to increase the plasticity of nanocrystalline alloys as compared to ordered grain boundaries. Here, the effect of an important structural descriptor, amorphous complexion thickness, on the plasticity and failure modes of nanocrystalline Cu-Zr is studied with in-situ compression testing, with over 50 micropillars tested. Two model materials were created that differ only in their complexion thickness, with one having a thicker complexion population than the other. The sample with thinner complexions was more likely to experience non-uniform plastic deformation in the form of localized plastic flow or shear banding. In contrast, the sample with thicker complexions displayed more homogeneous plasticity and higher damage tolerance; thicker amorphous complexions suppress localization by absorbing defects. This work demonstrates that increasing complexion thickness can be beneficial for stable plastic flow in nanocrystalline alloys, by improving resistance to strain localization and premature failure.

cond-mat.mtrl-sci↗

arXiv2025

Predicting void nucleation in microstructure with convolutional neural networks

Void nucleation in ductile materials subjected to high strain-rate loading remains a critical yet elusive phenomenon to understand. Traditional methods to understand void nucleation typically rely on experiments and molecular dynamics and do not capture the underlying factors leading to void nucleation. In this study, a convolutional neural network, specifically a U-Net enhanced with attention gates is developed, to predict void nucleation probability in pristine tantalum microstructures. The approach leverages a multi-channel input, incorporating four channels of grain orientations and an additional channel of grain boundary energy calculated via the lattice matching method. Void nucleation probability fields are determined from post-mortem micrographs and serve as ground truth, distinguishing void from no-void regions at the pixel level. Pixel-level class imbalance, commen in such images, is addressed by using Focal loss to guide the network's training to predict void nucleation sites more effectively. The model not only predicts void nucleation sites consistent with ground-truth but also reveals additional potential void nucleation sites, capturing the stochastic nature of void nucleation. This study shows that CNN-based models can predict void nucleation sites while considering combined interplay of factors such as grain boundary energy and grain orientation. In this way, machine learning can serve as a means to understand the underlying factors leading to void nucleation thereby contributing to a fundamental understanding of failure due to spallation in ductile materials.

cond-mat.mtrl-sci↗

arXiv2025

The structure and migration of twin boundaries in tetragonal $β$-Sn: an application of machine learning based interatomic potentials

Although atomistic simulations have contributed significantly to our understanding of twin boundary structure and migration in metals and alloys with hexagonal close packed (HCP) crystal structures, few direct atomistic studies of twinning have been conducted for other types of low symmetry materials, in large part due to a lack of reliable interatomic potentials. In this work, we examine twin boundary structure and migration in a tetragonal material, $β$-Sn, comparing high resolution Transmission Electron Microscopy (TEM) images of deformation twins in $β$-Sn to the results of direct atomistic simulations using multiple interatomic potentials. ML-based potentials developed in this work are found to give results consistent with our experimental data, revealing faceted twin boundary structures formed by the nucleation and motion of twinning disconnections. We use bicrystallographic methods in combination with atomistic simulations to analyze the structure, energy and shear coupled migration of observed twin facets in $β$-Sn. In analogy to Prismatic-Basal (PB/BP) interfaces in HCP metals, we discover low energy asymmetric Prismatic-A-plane (PA/AP) interfaces important to twin growth in $β$-Sn. A Moment Tensor Potential (MTP) and Rapid Artificial Neural Network (RANN) interatomic potential suitable for studying twinning and phase transformations in Sn are made publicly available as part of this work.

cond-mat.mtrl-sci↗

arXiv2024

Effect of helium bubbles on the mobility of edge dislocations in copper

Helium bubbles can form in materials upon exposure to irradiation. It is well known that the presence of helium bubbles can cause changes in the mechanical behavior of materials. To improve the lifetime of nuclear components, it is important to understand deformation mechanisms in helium-containing materials. In this work, we investigate the interactions between edge dislocations and helium bubbles in copper using molecular dynamics (MD) simulations. We focus on the effect of helium bubble pressure (equivalently, the helium-to-vacancy ratio) on the obstacle strength of helium bubbles and their interaction with dislocations. Our simulations predict significant differences in the interaction mechanisms as a function of helium bubble pressure. Specifically, bubbles with high internal pressure are found to exhibit weaker obstacle strength as compared to low-pressure bubbles of the same size due to the formation of super-jogs in the dislocation. Activation energies and rate constants extracted from the MD data confirm this transition in mechanism and enable upscaling of these phenomena to higher length-scale models.

cond-mat.mtrl-sci↗

arXiv2024

The structure and migration of heavily irradiated grain boundaries and dislocations in Ni in the athermal limit

The microstructural evolution at and near pre-existing grain boundaries (GBs) and dislocations in materials under high radiation doses is still poorly understood. In this work, we use the creation relaxation algorithm (CRA) developed for atomistic modeling of high-dose irradiation in bulk materials to probe the athermal limit of saturation of GB and dislocation core regions under irradiation in FCC Ni. We find that, upon continuously subjecting a single dislocation or GB to Frenkel pair creation in the athermal limit, a local steady state disordered defect structure is reached with excess properties that fluctuate around constant values. Case studies are given for a straight screw dislocation which elongates into a helix under irradiation and several types of low and high angle GBs, which exhibit coupled responses such as absorption of extrinsic dislocations, roughening and migration. A positive correlation is found between initial GB energy and the local steady state GB energy under irradiation across a wide variety of GB types. Metastable GB structures with similar density in the defect core region but different initial configurations are found to converge to the same limiting structure under CRA. The mechanical responses of pristine and irradiated dislocations and GB structures are compared under an applied shear stress. Irradiated screw and edge dislocations are found to exhibit a hardening response, migrating at larger flow stresses than their pristine counterparts. Mobile GBs are found to exhibit softening or hardening responses depending on GB character. Although some GBs recover their initial pristine structures upon migration outside of the radiation zone, many GBs sustain different flow stresses corresponding to altered mobile core structures.

cond-mat.mtrl-sci↗

arXiv2023

Machine Learning Based Approach to Predict Ductile Damage Model Parameters for Polycrystalline Metals

Damage models for ductile materials typically need to be parameterized, often with the appropriate parameters changing for a given material depending on the loading conditions. This can make parameterizing these models computationally expensive, since an inverse problem must be solved for each loading condition. Using standard inverse modeling techniques typically requires hundreds or thousands of high-fidelity computer simulations to estimate the optimal parameters. Additionally, the time of a human expert is required to set up the inverse model. Machine learning has recently emerged as an alternative approach to inverse modeling in these settings, where the machine learning model is trained in an offline manner and new parameters can be quickly generated on the fly, after training is complete. This work utilizes such a workflow to enable the rapid parameterization of a ductile damage model called TEPLA with a machine learning inverse model. The machine learning model can efficiently estimate the model parameters much faster, as compared to previously employed methods, such as Bayesian calibration. The results demonstrate good accuracy on a synthetic test dataset and is validated against experimental data.

cond-mat.mtrl-sci↗

arXiv2023

Properties of accelerating edge dislocations in arbitrary slip systems with reflection symmetry

We discuss the theoretical solution to the differential equations governing accelerating edge dislocations in anisotropic crystals. This is an important prerequisite to understanding high speed dislocation motion, including an open question about the existence of transonic dislocation speeds, and subsequently high rate plastic deformation in metals and other crystals.

cond-mat.mtrl-sci↗

arXiv2022

Limiting velocities and transonic dislocations in Mg

To accurately predict the mechanical response of materials, especially at high strain rates, it is important to account for dislocation velocities in these regimes. Under these extreme conditions, it has been hypothesized that dislocations can move faster than the speed of sound. However, the presence of such dislocations remains elusive due to challenges associated with measuring these experimentally. In this work, molecular dynamics simulations were used to investigate the dislocation velocities for the basal edge, basal screw, prismatic edge, and prismatic screw dislocations in Mg in the sub-, trans-, and supersonic regimes. Our results show that only prismatic edge dislocations achieve supersonic velocities. Furthermore, this work demonstrates that the discrepancy between the theoretical limiting velocity and the MD results for Mg is due to its sensitivity to large hydrostatic stress around the dislocation core, which was not the case for fcc metals such as Cu.

cond-mat.mtrl-sci↗

arXiv2020

Clarifying the definition of 'transonic' screw dislocations

A number of recent Molecular Dynamics (MD) simulations have demonstrated that screw dislocations in face centered cubic (fcc) metals can achieve stable steady state motion above the lowest shear wave speed ($v_\text{shear}$) which is parallel to their direction of motion (often referred to as transonic motion). This is in direct contrast to classical continuum analyses which predict a divergence in the elastic energy of the host material at a crystal geometry dependent `critical' velocity $v_\text{crit}$. Within this work, we first demonstrate through analytic analyses that the elastic energy of the host material diverges at a dislocation velocity ($v_\text{crit}$) which is greater than $v_\text{shear}$, i.e. $v_\text{crit} > v_\text{shear}$. We argue that it is this latter derived velocity ($v_\text{crit}$) which separates `subsonic' and `supersonic' regimes of dislocation motion in the analytic solution. In addition to our analyses, we also present a comprehensive suite of MD simulation results of steady state screw dislocation motion for a range of stresses and several cubic metals at both cryogenic and room temperatures. At room temperature, both our independent MD simulations and the earlier works find stable screw dislocation motion only below our derived $v_\text{crit}$. Nonetheless, in real-world polycrystalline materials $v_\text{crit}$ cannot be interpreted as a hard limit for subsonic dislocation motion. In fact, at very low temperatures our MD simulations of Cu at 10 Kelvin confirm a recent claim in the literature that true `supersonic' screw dislocations with dislocation velocities $v>v_\text{crit}$ are possible at very low temperatures.

cond-mat.mtrl-sci↗

arXiv2020

Automated discovery of a robust interatomic potential for aluminum

Accuracy of molecular dynamics simulations depends crucially on the interatomic potential used to generate forces. The gold standard would be first-principles quantum mechanics (QM) calculations, but these become prohibitively expensive at large simulation scales. Machine learning (ML) based potentials aim for faithful emulation of QM at drastically reduced computational cost. The accuracy and robustness of an ML potential is primarily limited by the quality and diversity of the training dataset. Using the principles of active learning (AL), we present a highly automated approach to dataset construction. The strategy is to use the ML potential under development to sample new atomic configurations and, whenever a configuration is reached for which the ML uncertainty is sufficiently large, collect new QM data. Here, we seek to push the limits of automation, removing as much expert knowledge from the AL process as possible. All sampling is performed using MD simulations starting from an initially disordered configuration, and undergoing non-equilibrium dynamics as driven by time-varying applied temperatures. We demonstrate this approach by building an ML potential for aluminum (ANI-Al). After many AL iterations, ANI-Al teaches itself to predict properties like the radial distribution function in melt, liquid-solid coexistence curve, and crystal properties such as defect energies and barriers. To demonstrate transferability, we perform a 1.3M atom shock simulation, and show that ANI-Al predictions agree very well with DFT calculations on local atomic environments sampled from the nonequilibrium dynamics. Interestingly, the configurations appearing in shock appear to have been well sampled in the AL training dataset, in a way that we illustrate visually.

cond-mat.mtrl-sci↗

arXiv2020

Combining Laue diffraction with Bragg coherent diffraction imaging at 34-ID-C

Measurement modalities in Bragg coherent diffraction imaging (BCDI) rely on finding signal from a single nanoscale crystal object, which satisfies the Bragg condition among a large number of arbitrarily oriented nanocrystals. However, even when the signal from a single Bragg reflection with (hkl) Miller indices is found, the crystallographic axes on the retrieved three-dimensional (3D) image of the crystal remain unknown, and thus, localizing in reciprocal space other Bragg reflections becomes in reality impossible or requires good knowledge of the orientation of the crystal. We report the commissioning of a movable double-bounce Si (111) monochromator at the 34-ID-C end station of the Advanced Photon Source, which aims at delivering multi-reflection BCDI as a standard tool in a single beamline instrument. The new instrument enables this through rapid switching from monochromatic to broadband (pink) beam permitting the use of Laue diffraction to determine crystal orientation. With a proper orientation matrix determined for the lattice, one can measure coherent diffraction near multiple Bragg peaks, thus providing sufficient information to image the full strain tensor in 3D. We discuss the design, concept of operation, the developed procedures for indexing Laue patterns, and automated measuring of Bragg coherent diffraction data from multiple reflections of the same nanocrystal.

physics.ins-det↗

arXiv2010

Structural disjoining potential for grain boundary premelting and grain coalescence from molecular-dynamics simulations

We describe a molecular dynamics framework for the direct calculation of the short-ranged structural forces underlying grain-boundary premelting and grain-coalescence in solidification. The method is applied in a comparative study of (i) a Sigma 9 <115> 120 degress twist and (ii) a Sigma 9 <110> {411} symmetric tilt boundary in a classical embedded-atom model of elemental Ni. Although both boundaries feature highly disordered structures near the melting point, the nature of the temperature dependence of the width of the disordered regions in these boundaries is qualitatively different. The former boundary displays behavior consistent with a logarithmically diverging premelted layer thickness as the melting temperature is approached from below, while the latter displays behavior featuring a finite grain-boundary width at the melting point. It is demonstrated that both types of behavior can be quantitatively described within a sharp-interface thermodynamic formalism involving a width-dependent interfacial free energy, referred to as the disjoining potential. The disjoining potential for boundary (i) is calculated to display a monotonic exponential dependence on width, while that of boundary (ii) features a weak attractive minimum. The results of this work are discussed in relation to recent simulation and theoretical studies of the thermodynamic forces underlying grain-boundary premelting.

cond-mat.mtrl-sci↗