arXiv subjects

Saryu J. Fensin

Publications and source records attributed to Saryu J. Fensin.

7 recordsLinked to original sources

arXiv2025

Bayesian inference and uncertainty quantification for modeling of body-centered-cubic single crystals

Uncertainties in the high-dimensional space of material parameters pose challenges for the predictive modeling of bcc single crystals, especially under extreme loading conditions. In this work, we identify the key physical assumptions and associated uncertainties in constitutive models that describe the deformation behavior of bcc single crystal molybdenum subjected to quasi-static to shock loading conditions. We employ two representative physics-based bcc single crystal plasticity models taken from our previous work (Nguyen et al. 2021a; Lee et al. 2023b), each prioritizing different key deformation mechanisms. The Bayesian model calibration (BMC) is used for probabilistic estimates of material parameters in both bcc crystal plasticity models. In conjunction with the BMC procedure, the global sensitivity analysis is conducted to quantify the impact of uncertainties in the material parameters on the key simulation results of quasi-static to shock responses. The sensitivity indices at various loading conditions clearly illustrate the physical basis underlying the predictive capabilities of the two distinct bcc crystal plasticity models at low to high strain rates. Both of the calibrated bcc models are then further validated beyond the calibration regime, by which we further identify critical physical mechanisms that govern the transient elastic-plastic responses of single crystal molybdenum under shock loading. The statistical inference framework demonstrated here facilitates the further development of continuum crystal plasticity models that account for a broad range of deformation mechanisms.

cond-mat.mtrl-sci↗

arXiv2025

Achieving DFT accuracy in short range ordering and stacking fault energy using moment tensor potential for CoCrFeNi and CoCrNi

Medium-entropy alloys (MEAs) such as CoCrFeNi and CoCrNi are promising structural materials owing to their outstanding mechanical and thermal properties, which arise from complex chemical disorder and atomic-scale interactions. Although density functional theory (DFT) has provided fundamental insights into these systems, its high computational cost limits exploration of large-scale phenomena. Classical interatomic potentials have been used to address this gap but often lack the fidelity needed to capture many-body interactions and chemical short-range ordering (CSRO) effects. In this work, we developed a machine-learned Moment Tensor Potential (MTP) to bridge accuracy and efficiency. The MTP was trained on a comprehensive DFT database spanning unary to quaternary configurations and reproduces energies, forces, and stresses with near-DFT accuracy across diverse structural and chemical environments. It accurately predicts elastic properties and recovers compositional trends in bulk and shear moduli in agreement with DFT. Hybrid Monte Carlo/molecular dynamics simulations capture CSRO, reproducing key DFT-reported features including Cr-Cr and Fe-Fe repulsion and Ni-Cr ordering. Stacking fault energetics were modeled, yielding ISF energies near 54 mJ/m2 for CoCrNi and 36 mJ/m2 for CoCrFeNi, consistent with DFT predictions. Local chemical environment effects on stacking faults were also resolved: Co-rich planes reduce, whereas Cr- or Fe-rich planes increase, the stacking fault energy. By enabling large-scale, high-fidelity simulations at a fraction of DFT's cost, the developed MTP provides a robust framework for predictive modeling of thermodynamic stability, defect behavior, and mechanical response of FCC MEAs.

cond-mat.mtrl-sci↗

arXiv2025

Evaluating Moment Tensor Potential in Ag-Cu Alloy: Accuracy, Transferability, and Phase Diagram Fidelity

A Moment Tensor Potential (MTP) has been developed for the Cu-Ag binary alloy and its accuracy, transferability, and thermodynamic fidelity evaluated. The model was trained on a diverse dataset encompassing solid, liquid, and interfacial configurations derived from density functional theory (DFT) calculations. Benchmarking against experiment and DFT data demonstrated significant improvements over the widely used classical Embedded Atom Method (EAM) potential, particularly in predicting defect energetics, surface properties, and the eutectic phase diagram. Despite a slight underestimation of Ag's melting point, the MTP model achieved consistent accuracy across elemental and binary systems without direct fitting to high-temperature phase transitions. The predicted eutectic temperature and composition were found in close agreement with experimental observations. These results establish MTP as a robust framework for modeling immiscible metallic systems and pave the way for its integration into large-scale atomistic simulations where both fidelity and generalizability are essential.

cond-mat.mtrl-sci↗

arXiv2024

Exploring the relation between transonic dislocation glide and stacking fault width in FCC metals

Theory predicts limiting gliding velocities that dislocations cannot overcome. Computational and recent experiments have shown that these limiting velocities are soft barriers and dislocations can reach transonic speeds in high rate plastic deformation scenarios. In this paper we systematically examine the mobility of edge and screw dislocations in several face centered cubic (FCC) metals (Al, Au, Pt, and Ni) in the extreme large-applied-stress regime using MD simulations. Our results show that edge dislocations are more likely to move at transonic velocities due to their high mobility and lower limiting velocity than screw dislocations. Importantly, among the considered FCC metals, the dislocation core structure determines the dislocation's ability to reach transonic velocities. This is likely due to the variation in stacking fault width (SFW) due to relativistic effects near the limiting velocities.

cond-mat.mtrl-sci↗

arXiv2024

Predicting Ti-Al Binary Phase Diagram with an Artificial Neural Network Potential

The microstructure of the Ti-Al binary system is an area of great interest as it affects material properties and plasticity. Phase transformations induce microstructural changes; therefore, accurately modeling the phase transformations of the Ti-Al system is necessary to describe plasticity. Interatomic potentials can be a powerful tool to model how materials behave; however, existing potentials lack accuracy in certain aspects. While classical potentials like the Embedded Atom Method (EAM) and Modified Embedded Atom Method (MEAM) perform adequately for modeling dilute Al solute within Ti's $α$ phase, they struggle with accurately predicting plasiticity. In particular, they struggle with stacking fault energies in intermetallics and to some extent elastic properties. This hinders their effectiveness in investigating the plastic behavior of formed intermetallics in Ti-Al alloys. Classical potentials also fail to predict the $α$ to $β$ phase boundary. Existing machine learning (ML) potentials reproduce the properties of formed intermetallics with density functional theory (DFT) but do not examine the $α$ to $β$ or $α$ to D0$_{19}$ phase boundaries. This work uses a rapid artificial neural network (RANN) framework to produce a neural network potential for the Ti-Al binary system. This potential is capable of reproducing the Ti-Al binary phase diagram up to 50$\%$ Al concentration. The present interatomic potential ensures stability and allows results near the accuracy of DFT. Using Monte Carlo simulations, RANN potential accurately predicts the $α$ to $β$ and $α$ to D0$_{19}$ phase transitions. The current potential also exhibits accurate elastic constants and stacking fault energies for the L1$_0$ and D0$_{19}$ phases.

cond-mat.mtrl-sci↗

arXiv2024

Learning from metastable grain boundaries

Grain boundaries (GBs) govern critical properties of polycrystals. Although significant advancements have been made in characterizing minimum energy GBs, real GBs are seldom found in such states, making it challenging to establish structure-property relationships. This diversity of atomic arrangements in metastable states motivates using data-driven methods to establish these relationships. In this study, we utilize a vast atomistic database (~5000) of minimum energy and metastable states of symmetric tilt copper GBs, combined with physically-motivated local atomic environment (LAE) descriptors (Strain Functional Descriptors, SFDs) to predict GB properties. Our regression models exhibit robust predictive capabilities using only 19 descriptors, generalizing to atomic environments in nanocrystals. A significant highlight of our work is integration of an unsupervised method with SFDs to elucidate LAEs at GBs and their role in determining properties. Our research underscores the role of a physics-based representation of LAEs and efficacy of data-driven methods in establishing GB structure-property relationships.

cond-mat.mtrl-sci↗

arXiv2022

Perspectives on Novel Refractory Amorphous High-Entropy Alloys in Extreme Environments

Two new refractory amorphous high-entropy alloys (RAHEAs) within the W--Ta--Cr--V and W--Ta--Cr--V--Hf systems were herein synthesized using magnetron-sputtering and tested under high-temperature annealing and displacing irradiation using \textit{in situ} Transmission Electron Microscopy. While the WTaCrV RAHEA was found to be unstable under such tests, additions of Hf in this system composing a new quinary WTaCrVHf RAHEA was found to be a route to achieve stability both under annealing and irradiation. A new effect of nanoprecipitate reassembling observed to take place within the WTaCrVHf RAHEA under irradiation indicates that a duplex microstructure composed of an amorphous matrix with crystalline nanometer-sized precipitates enhances the radiation response of the system. It is demonstrated that tunable chemical complexity arises as a new alloy design strategy to foster the use of novel RAHEAs within extreme environments. New perspectives for the alloy design and application of chemically-complex amorphous metallic alloys in extreme environments are presented with focus on their thermodynamic phase stability when subjected to high-temperature annealing and displacing irradiation.

cond-mat.mtrl-sci↗