arXiv subjects

Veera Sundararaghavan

Publications and source records attributed to Veera Sundararaghavan.

At least 19 recordsLinked to original sources

arXiv2026

Uncertainty quantification of fatigue initiation life for powder bed fusion metal additive manufacturing

Predicting fatigue life with quantified uncertainties is essential for the qualification of critical components produced by laser-based powder bed fusion additive manufacturing. We present a framework that propagates microstructure and defect uncertainties directly to a fatigue initiation life distribution for a specific part. In particular, microstructure and defect characterizations are obtained from electron backscatter diffraction and micro-computed tomography scan data, which in turn inform three physics-based simulations yielding the fatigue-affecting quantities: the elastic energy release rate, the surface energy along the crack path, and the fatigue indicator parameter. Accounting for the uncertainties in these quantities and the high correlations among them due to the shared underlying microstructure, we derive a closed-form probability density function for the fatigue initiation life. This provides an analytical distribution instead of conservative deterministic predictions and enables more informed decision making for the qualification and deployment of additively manufactured components. Applying the framework to 316L stainless steel parts produced by an EOS M290 laser powder bed fusion machine, we find that both grain sizes and void distributions influence the fatigue initiation life distribution. Specifically, for a fixed total void volume fraction, larger grain sizes cause a marginal reduction in fatigue initiation life, and a population of many small voids is more favorable for fatigue life than fewer, larger voids of equivalent total volume.

stat.AP↗

arXiv2026

Element-Specific Solute Trapping and Grain Structure Evolution during Laser Powder Bed Fusion of Multicomponent Alloys

Under the rapid solidification conditions of laser powder bed fusion (LPBF), solute trapping manifests in an element-specific manner, altering nonequilibrium partitioning, constitutional undercooling, and grain selection behavior in multicomponent alloys. Here, we elucidate the mechanisms by which element-specific solute trapping governs nucleation behavior and grain structure evolution during LPBF demonstrated on a SS316L. This requires quantitative description of nonequilibrium multicomponent thermodynamics and grain evolution across broad LPBF solidification conditions, which is achieved through a CALPHAD-informed Gaussian Process Regression (GPR)-assisted Phase-Field (PF) approach. The predicted transitions in grain morphology and grain size are validated against EBSD measurements under multiple LPBF processing conditions. Results demonstrate that increasing solidification rate drives a composition-dependent transition from solute diffusion-controlled nucleation to solute trapping-controlled grain growth, where nonequilibrium solute redistribution intensified by solute trapping suppresses equiaxed grain formation despite high cooling rates. Quantitative decomposition of multicomponent undercooling further reveals distinct element-specific sensitivities to solute trapping, where C, Cr, and Mo remain dominant contributors to the overall undercooling, while the undercooling contribution of low-partitioning elements such as S and P are strongly suppressed relative to their equilibrium values under rapid solidification conditions. These results reveal how element-specific solute trapping governs grain selection in multicomponent alloys, providing a mechanistic basis for alloy design under nonequilibrium solidification conditions.

cond-mat.mtrl-sci↗

arXiv2026

From Data to Theory: Autonomous Large Language Model Agents for Materials Science

We present an autonomous large language model (LLM) agent for end-to-end, data-driven materials theory development. The model can choose an equation form, generate and run its own code, and test how well the theory matches the data without human intervention. The framework combines step-by-step reasoning with expert-supplied tools, allowing the agent to adjust its approach as needed while keeping a clear record of its decisions. For well-established materials relationships such as the Hall-Petch equation and Paris law, the agent correctly identifies the governing equation and makes reliable predictions on new datasets. For more specialized relationships, such as Kuhn's equation for the HOMO-LUMO gap of conjugated molecules as a function of length, performance depends more strongly on the underlying model, with GPT-5 showing better recovery of the correct equation. Beyond known theories, the agent can also suggest new predictive relationships, illustrated here by a strain-dependent law for changes in the HOMO-LUMO gap. At the same time, the results show that careful validation remains essential, because the agent can still return incorrect, incomplete, or inconsistent equations even when the numerical fit appears strong. Overall, these results highlight both the promise and the current limitations of autonomous LLM agents for AI-assisted scientific modeling and discovery.

cs.AI↗

arXiv2026

Understanding the influence of yttrium on the dominant twinning mode and local mechanical field evolution in extruded Mg-Y alloys

Twinning is a primary deformation mechanism in Mg alloys. This study focuses on tension twins during uniaxial compression of Mg-Y alloys, with three key aspects: the orientation specificity of twin grains, the relative evolution of CRSS with increasing Y content, and the local stress and strain evolution at twin sites. Experimental characterization and crystal plasticity modeling were performed. In Mg-7wt.%Y, TT2-{112-1} tension twins were observed in addition to the common TT1-{101-2} twins. Increasing Y suppressed TT1 formation while promoting TT2 activity. A previously unreported group of crystallographic orientations with a higher global Schmid factor for slip was identified, which exhibited TT1 twinning with increasing compression strain. To elucidate Y effects on twin activity and local mechanical fields, both TT1 and TT2 tension twin modes were incorporated into PRISMS-Plasticity, an open-source, finite element-based crystal plasticity solver. Four binary Mg-Y alloys were modeled under compression, and statistical analysis was conducted to correlate initial orientations, stress-strain distributions, and twin activities as functions of Y concentration. The plasticity analysis revealed that increasing Y decreases the CRSS ratio of prismatic and pyramidal slip relative to TT1 twinning, while the slip-to-twin CRSS ratio for TT2 increases, thereby serving as a potential indicator of differential twin activity with Y addition in Mg alloys. Additionally, despite their small volume fraction, TT2 twin sites were predicted higher local strain accumulation locally, relative to the representative volume element and TT1 twins, suggesting their potential influence on localized phenomena such as recrystallization or twin nucleation. These findings provide insight into local mechanical behavior in Mg alloys and support alloy design for advanced engineering applications.

cond-mat.mtrl-sci↗

arXiv2026

Understanding the kinetics of static recrystallization in Mg-Zn-Ca alloys using an integrated PRISMS simulation framework

Recrystallization is a phenomenon in which a plastically deformed polycrystalline microstructure with a high dislocation density transforms into another that has low dislocation density. This evolution is driven by the stored energy in dislocations, rather than grain growth driven by grain boundary energy alone. One difficulty in quantitative modeling of recrystallization is the uncertainty in material parameters, which can be addressed by integration of experimental data into simulations. In this work, we compare simulated static recrystallization dynamics of a Mg-3Zn-0.1Ca wt.% alloy to experiments involving thermomechanical processing followed by measurements of the recrystallization fraction over time. The simulations are performed by combining PRISMS software for crystal plasticity and phase-field models (PRISMS-Plasticity and PRISMS-PF, respectively) in an integrated computational materials engineering framework. At 20% strain and annealing at 350 {\deg}C, the model accurately describes recrystallization dynamics up to a mobility-dependent time scale factor. While the average grain boundary mobility and the fraction of plastic work converted into stored energy are not precisely known, by fitting simulations to experimental data, we show that the average grain boundary mobility can be determined if the fraction of plastic work converted to stored energy is known, or vice versa. For low annealing temperatures, we observe a discrepancy between the model and experiments in the late stages of recrystallization, where a slowdown in recrystallization kinetics occurs in the experiments. We discuss possible sources of this slowdown and propose additional physical mechanisms that need to be accounted for in the model to improve its predictions.

cond-mat.mtrl-sci↗

arXiv2026

ELAS3D-Xtal: An OpenMP-accelerated crystal elasticity solver with automated experiment-driven microstructure generation

This paper introduces ELAS3D-Xtal, a high-performance Fortran/OpenMP upgrade of the NIST ELAS3D voxel-based finite element solver for computing 3D elastic fields in polycrystals with defects. The code supports crystal anisotropy by precomputing rotated stiffness tensors from user-specified orientations and solves the equilibrium problem with a matrix-free, OpenMP-parallel preconditioned conjugate-gradient (PCG) method using a point-block Jacobi preconditioner. On a single shared-memory multicore PC, OpenMP threading accelerates the baseline CG solver by ~10X, while the block-preconditioned CG solver achieves 53-61X speedup relative to the serial CG baseline for meshes from 100^3 to 500^3 voxels (scaling to domains up to 800^3 voxels). Accuracy is validated against the analytical Eshelby inclusion solution. ELAS3D-Xtal also integrates microstructure construction, including statistically calibrated polycrystal generation via spatial filtering and parallel voxel-to-grain assignment, direct pore insertion from XCT centroid/radius data, and texture assignment. Full-field phase, orientation, and stress outputs are written in HDF5 to enable scalable post-processing and defect-mechanics workflows. Applications are demonstrated for (i) anisotropy-controlled defect-scale stress fields and (ii) LPBF SS316L microstructures with gas, lack-of-fusion, and keyhole pore morphologies.

cond-mat.mtrl-sci↗

arXiv2026

Comparative Analysis of Plasticity-based GND Density Estimation Methods in Crystal Plasticity Finite Element Models

In crystal plasticity finite element (CPFE) simulations, accurately quantifying geometrically necessary dislocations (GNDs) is critical for capturing strain gradients in polycrystals. We compare different methods for quantifying GNDs, all of which originate from the Nye tensor, which is computed as the curl of the plastic deformation gradient. The projection technique directly decomposes the Nye tensor onto individual screw and edge dislocation components to compute GNDs. This approach requires converting a nine-component Nye tensor into densities for a larger number of dislocation systems, a fundamentally underdetermined (non-unique) process, which is resolved using $L2$ minimization. In contrast, when employing CPFE analysis, one could directly compute dislocation densities on each slip system using shear gradients. Projection and slip gradient methods are compared with respect to their prediction of GNDs with changing grain size, strain, and grain neighborhoods, including multigrain junctions. Although these techniques match analytical GND densities for single slip, single crystal deformation, and are consistent with anticipated overall GND trends, we find that the GND densities from projection techniques are significantly lower than those predicted from CPFE-based slip gradients in polycrystals. A suggested improvement of only using the active dislocation systems in the projection technique almost entirely resolved this mismatch.

cond-mat.mtrl-sci↗

arXiv2025

Actuation mechanisms in twisted and coiled polymer actuators using finite element model

Twisted and coiled polymer actuators (TCPAs) offer the advantages of large stroke and large specific work as compared to other actuators. There have been extensive experimental investigations towards understanding their actuation response, however, a computational model with full material description is not utilized to probe into the underlying mechanisms responsible for their large actuation. In this work, we develop a three-dimensional finite element model that includes the physics of the fabrication process to simulate the actuation of TCPA under various loading and boundary conditions. The model is validated against the experimental data and used to explore the factors responsible for actuation under free and isobaric conditions. The model captures the physics of the angle of twist in the fiber and the distinction between the homochiral and heterochiral nature of TCPA actuation response. The simulations show that the anisotropy in the thermal expansion coefficient (CTE) matrix plays a major role in large actuation irrespective of the anisotropy or isotropy in the elasticity tensor. We further investigate the extent of anisotropy in thermal expansion and the parametric studies show that the key for TCPA actuation is the absolute value of mismatch in thermal expansion even if the material has positive or negative CTE in both directions of the fiber. Furthermore, we propose a new shell-core composite-based TCPA concept by combining the epoxy and hollow Nylon tubes to suppress the creep in TCPA. The results show that the volume fraction of epoxy-core can be tuned to attain a desired actuation while offering a stiffer and creep-resistant response. This framework provides a wider application for probing various kinds of TCPAs and enhancing their actuation performance.

physics.comp-ph↗

arXiv2024

Three-dimensional nucleation and growth of deformation twins in magnesium

At two-thirds the weight of aluminum, magnesium alloys have the potential to significantly reduce the fuel consumption of transportation vehicles. These advancements depend on our ability to optimize the desirable versus undesirable effects of deformation twins: three dimensional (3D) microstructural domains that form under mechanical stresses. Previously only characterized using surface or thin-film measurements, here, we present the first 3D in-situ characterization of deformation twinning inside an embedded grain over mesoscopic fields of view using dark-field X-ray microscopy supported by crystal plasticity finite element analysis. The results reveal the important role of triple junctions on twin nucleation, that twin growth behavior is irregular and can occur in several directions simultaneously, and that twin-grain and twin-twin junctions are the sites of localized dislocation accumulation, a necessary precursor to crack initiation.

cond-mat.mtrl-sci↗

arXiv2024

Lagrangian Neural Networks for Reversible Dissipative Evolution

There is a growing attention given to utilizing Lagrangian and Hamiltonian mechanics with network training in order to incorporate physics into the network. Most commonly, conservative systems are modeled, in which there are no frictional losses, so the system may be run forward and backward in time without requiring regularization. This work addresses systems in which the reverse direction is ill-posed because of the dissipation that occurs in forward evolution. The novelty is the use of Morse-Feshbach Lagrangian, which models dissipative dynamics by doubling the number of dimensions of the system in order to create a mirror latent representation that would counterbalance the dissipation of the observable system, making it a conservative system, albeit embedded in a larger space. We start with their formal approach by redefining a new Dissipative Lagrangian, such that the unknown matrices in the Euler-Lagrange's equations arise as partial derivatives of the Lagrangian with respect to only the observables. We then train a network from simulated training data for dissipative systems such as Fickian diffusion that arise in materials sciences. It is shown by experiments that the systems can be evolved in both forward and reverse directions without regularization beyond that provided by the Morse-Feshbach Lagrangian. Experiments of dissipative systems, such as Fickian diffusion, demonstrate the degree to which dynamics can be reversed.

cs.LG↗

arXiv2023

Understanding Creep in Vitrimers: Insights from Molecular Dynamics Simulations

Vitrimers offer a promising sustainable alternative to conventional epoxies due to their recyclability. Vitrimers are covalent adaptive networks where some bonds can break and reform above the vitrimer transition temperature. While this can lead to desirable behavior such as malleability, this also leads to undesirable rheological behavior such as low-temperature creep. In this work, we investigate the molecular mechanisms of the creep of vitrimers using molecular dynamics simulations. The interplay between dynamic bonding with mechanical loading is modeled using a topology-based reaction scheme. The creep behavior is compared against cross-linked epoxies with dynamic reactions to understand the unique aspects related to dynamic bonding. It is found that the free volume that arises from tensile loads is reduced in vitrimers through dynamic bond rearrangement. An important feature that explains the difference in secondary creep behavior between conventional epoxies and vitrimers is the orientation of the dynamic bonds during loading. In vitrimers, the dynamic bonds preferentially align orthogonal to the loading axis, decreasing the axial stiffness during secondary creep, resulting in larger creep strain compared to epoxies. Over longer timescales, such increased strain leads to void growth, resulting in tertiary creep. Thus, chemistry changes or additives that can prevent the initial realignment of dynamic bonds, and therefore subsequent void growth, can be an effective strategy to mitigate creep in vitrimers.

cond-mat.soft↗

arXiv2023

Computational study of optical absorption spectra of helicenes as applied to strain sensing

Helicenes, a class of organic molecules consisting of ortho-fused benzene rings in a spring-like configuration have found several interesting applications in nonlinear optical materials and opto-electronic devices. Under the action of strain, i.e., via mechanical stretching or compression, the optical absorption spectra of helicenes change which can be employed for strain sensing. The present study presents a detailed investigation of the optical absorption spectra of helicenes using density functional theory along with calculations of the changes in the spectra during mechanical axial stretching or compression of helicenes. The electronic band gap followed a non-symmetric parabolic form with the amount of applied strain. A lowering of the gap in stretched or compressed helicenes compared to the pristine helicene was observed. The compressed state shows a smaller energy gap compared to tension for the same strain magnitude. A detailed inspection of the optical absorption spectra shows that compressive states show significantly lower absorption at higher optical energies (shorter wavelengths) which can provide greater sensitivity to the strain measurement.

physics.chem-ph↗

arXiv2023

Towards Microstructural State Variables in Materials Systems

The vast combination of material properties seen in nature are achieved by the complexity of the material microstructure. Advanced characterization and physics based simulation techniques have led to generation of extremely large microstructural datasets. There is a need for machine learning techniques that can manage data complexity by capturing the maximal amount of information about the microstructure using the least number of variables. This paper aims to formulate dimensionality and state variable estimation techniques focused on reducing microstructural image data. It is shown that local dimensionality estimation based on nearest neighbors tend to give consistent dimension estimates for natural images for all p-Minkowski distances. However, it is found that dimensionality estimates have a systematic error for low-bit depth microstructural images. The use of Manhattan distance to alleviate this issue is demonstrated. It is also shown that stacked autoencoders can reconstruct the generator space of high dimensional microstructural data and provide a sparse set of state variables to fully describe the variability in material microstructures.

cs.LG↗

arXiv2022

Generative and discriminative training of Boltzmann machine through Quantum annealing

A hybrid quantum-classical method for learning Boltzmann machines (BM) for a generative and discriminative task is presented. Boltzmann machines are undirected graphs with a network of visible and hidden nodes where the former is used as the reading site while the latter is used to manipulate visible states' probability. In Generative BM, the samples of visible data imitate the probability distribution of a given data set. In contrast, the visible sites of discriminative BM are treated as Input/Output (I/O) reading sites where the conditional probability of output state is optimized for a given set of input states. The cost function for learning BM is defined as a weighted sum of Kullback-Leibler (KL) divergence and Negative conditional Log-Likelihood (NCLL), adjusted using a hyperparamter. Here, the KL Divergence is the cost for generative learning, and NCLL is the cost for discriminative learning. A Stochastic Newton-Raphson optimization scheme is presented. The gradients and the Hessians are approximated using direct samples of BM obtained through Quantum annealing (QA). Quantum annealers are hardware representing the physics of the Ising model that operates on low but finite temperature. This temperature affects the probability distribution of the BM; however, its value is unknown. Previous efforts have focused on estimating this unknown temperature through regression of theoretical Boltzmann energies of sampled states with the probability of states sampled by the actual hardware. This assumes that the control parameter change does not affect the system temperature, however, this is not usually the case. Instead, an approach that works on the probability distribution of samples, instead of the energies, is proposed to estimate the optimal parameter set. This ensures that the optimal set can be obtained from a single run.

quant-ph↗

arXiv2021

Bandgap optimization in combinatorial graphs with tailored ground states: Application in Quantum annealing

A mixed-integer linear programming (MILP) formulation is presented for parameter estimation of the Potts model. Two algorithms are developed; the first method estimates the parameters such that the set of ground states replicate the user-prescribed data set; the second method allows the user to prescribe the ground states multiplicity. In both instances, the optimization process ensures that the bandgap is maximized. Consequently, the model parameter efficiently describes the user data for a broad range of temperatures. This is useful in the development of energy-based graph models to be simulated on Quantum annealing hardware where the exact simulation temperature is unknown. Computationally, the memory requirement in this method grows exponentially with the graph size. Therefore, this method can only be practically applied to small graphs. Such applications include learning of small generative classifiers and spin-lattice model with energy described by Ising hamiltonian. Learning large data sets poses no extra cost to this method; however, applications involving the learning of high dimensional data are out of scope.

cs.DS↗

arXiv2021

Understanding defect structures in metal additive manufacturing via molecular dynamics

Additive manufacturing of a single crystalline metallic column is studied using molecular dynamics simulations. In the model, a melt pool is incrementally added and cooled to a target temperature under isobaric conditions to build a metallic column from bottom up. Common neighbor analysis (CNA) is used to observe the evolution of atomic scale defects during this process. The solidification is seen to proceed along two directions for an added molten layer. The molten layer in contact with the cooler lattice has a fast solidification front that competes with the slower solidification front starting from the top layer. Defect structure formed strongly depends on the speeds of the two competing solidification fronts. Up to a critical layer thickness, defect free single crystals are obtained as the faster solidification front reaches the top of the melt pool before the initiation of the slower front from the top. Slower cooling rates lead to reduction in defects, however, the benefits diminish below a critical rate. Defect content can be significantly reduced by raising the temperature of the powder bed to a critical temperature. This temperature is determined by two competing mechanisms: slower cooling rates at higher temperatures competing against increase in amorphousness as one gets closer to the melting point. Finally, effect of added soft inclusion (SiS2) and a hard inclusion (SiC) on the defect structure is studied. Hard inclusions lead to retained defect structure while soft inclusions reduce defective content compared to a pure metal.

cond-mat.mtrl-sci↗

arXiv2020

Modeling Self-Healing Behavior of Vitrimers using Molecular Dynamics with Dynamic Cross-Linking Capability

Vitrimers are a special class of polymers that undergo dynamic cross-linking under thermal stimuli. Their ability to exchange covalent bonds can be harnessed to mitigate damage in a composite or to achieve recyclable aerospace composites. This work addresses the primary challenge of modeling dynamic cross-linking reactions in vitrimers during thermomechanical loading. Dynamic bond exchange reaction probability change during heating and its effect on dilatometric and mechanical response are simulated for the first time in large scale molecular dynamics simulations. Healing of damage under thermal cycling is computed with mechanical properties predicted before and after self--healing.

cond-mat.mtrl-sci↗

arXiv2020

Database development and exploration of microstructure versus process relationships using variational autoencoders

The paper demonstrates the use of variational autoencoders for graphical representation of a large database containing process-microstructure relationships. Correlating microstructural features to processing is an essential first step to answer the difficult problem of process sequence design. In this paper, a large database of 346,200 orientation distribution functions resulting from a variety of process sequences is constructed, where each sequence comprises up to four stages of tension, compression and rolling along different directions in various permutations. This opensource database is constructed for collaborative development of process design algorithms. The paper demonstrates a novel application of the large database: graphical representation of texture-process relationships. A variational autoencoder is used to reduce the entire database to a two dimensional latent space where variations in processes and properties can be visualized. Using proximity analysis in this latent space, we can quickly unearth multiple process solutions to the problem of texture or property design.

physics.comp-ph↗