arXiv subjects

Chuang Deng

Publications and source records attributed to Chuang Deng.

18 recordsLinked to original sources

arXiv2026

High-throughput thermodynamic screening of oxide-scale adhesion across the CoCrFeMnNiAl high-entropy alloys

One significant benefit of reactive element (RE) additions is the colossal improvement in oxide-scale retention during high-temperature oxidation. Selecting optimal RE dopants in high-entropy alloys remains empirical because the relevant thermodynamic landscape is inaccessible to first-principles at the required compositional resolution. Here we apply the macroscopic atom model, coupled with McLean isotherm and Guttmann models, to screen adhesion across nine CoCrFeMnNiAl sub-families at \ce{Cr2O3} and \ce{Al2O3} interfaces, ranking five REs (Hf, Y, Zr, La, Ti) for segregation, adhesion enhancement, and sulfur displacement. The screening reveals an oxide-dependent ranking inversion, with Hf dominating at \ce{Cr2O3} and La dominating at \ce{Al2O3}, driven by the interplay between RE--O and RE--matrix interaction enthalpies. Mn-containing alloys exhibit intrinsic sulfur resistance, consistent with their experimentally observed oxidation characteristics. A sulfur immunity phase diagram identifies compositions with Al~+~Mn~$\gtrsim$~25~at\% as thermodynamically immune to S-induced adhesion loss. All crossover concentrations collapse onto a universal exponential governed by the segregation enthalpy difference, providing a transferable design rule. Inverse design identifies \ce{Co16Cr16Fe16Ni16Al35} as the optimal S-immune composition with $W_\text{sep} = 5.95$~J/m$^2$ without RE doping.

cond-mat.mtrl-sci↗

arXiv2026

Implicit size dependence of the valence electron concentration criterion in high-entropy alloys

The valence electron concentration (VEC) is the most widely used predictor of FCC against BCC stability in high-entropy alloys (HEAs), yet it is a compositional average carrying no information about atomic size. Using the macroscopic atom model, we show that the mixing enthalpy is almost size-blind, shifting by less than 6\% even when constituent volumes differ by a factor of 2, so that size can act only on the electron count. That action equals exactly the covariance of the atomic surface $V^{2/3}$ with the valence electron count, divided by its mean. This covariance is not free. Volume and valence are strongly anti-correlated across the elements used to build HEAs, so the size-corrected count is an affine rescaling of VEC over 265 characterized alloys and improves no prediction. VEC already encodes atomic size, which explains its success and locates its failure among large, electron-rich elements. Chemistry sets the enthalpy through one switch element.

cond-mat.mtrl-sci↗

arXiv2026

Solidification-cell confinement of domain-wall pinning in additively manufactured ferromagnets

As-built printed ferromagnets typically exhibit higher coercivity than optimized wrought materials, yet existing explanations rely on empirical fits or costly simulations. Herein, we provide a missing analytical theory that links print parameters directly to cooling rates, cellular spacing, dislocation density, and domain-wall pinning coercivity. Informed by metallographic grain data and using a single fitted constant, our model predicts six experimental datasets for pure Fe, Fe-6.9Si, and a multicomponent alloy within a factor of 1.9. We demonstrate that configurational lattice distortion is negligible, implying that single-phase printed alloys follow dilute-pinning laws. Critically, we introduce a confinement factor, $E=\sqrt{\lambda_{c}/2\delta_{w}}$, proving that solidification-induced dislocation packing makes cellular microstructures harder than conventionally cold-worked metals. The framework enables an alloy-sensitivity map to screen and rank compositions before manufacturing.

cond-mat.mtrl-sci↗

arXiv2026

Dominant-pair free energies predict phase selection in high-entropy alloys

Phase selection in multicomponent alloys is governed by the competition between entropic stabilization of disordered solutions and enthalpic driving forces for chemical ordering. However, widely used parametric criteria reduce it to a single scalar, carrying no explicit free energy for any competing ordered phase. Herein, we develop a thermodynamic framework based on the semi-empirical macroscopic atom model and the Dinsdale lattice stability database to fill this gap. We show that a dominant-pair mechanism, in which the Al-transition-metal interaction family dominates the ordering enthalpy, enables the complex multicomponent B2-ordering problem to be reduced to an effective pseudo-binary system with an analytically evaluated Bragg-Williams free energy. Combined with a minimum-free-energy classifier, the framework predicts the lowest-energy phase as a function of composition and temperature. This provides continuous phase stability maps rather than the single-value predictions of conventional descriptors. Demonstrated on high-entropy alloys using a dataset of 269 experimentally characterized samples, the model outperforms widely used phase-selection criteria in the class-balanced macro-F1 metric and achieves 77.9% on the well-posed three-class task, outperforming the valence electron concentration criterion. The model is general by construction and computationally efficient for predicting phase stability in multicomponent alloys over a broad range of compositions and temperatures.

cond-mat.mtrl-sci↗

arXiv2026

Predicting co-segregation in alloys with solute-solute interactions

The co-segregation of impurities in systems with multiple solute species has been widely recognized as an effective strategy for tailoring material properties. However, reliable predictions of co-segregation behavior remain a significant challenge for alloy design in these systems. In this work, we develop an extended dual-solute (DS) segregation framework to semi-quantitatively predict co-segregation behavior with solute-solute interactions, including both homoatomic and heteroatomic contributions. A machine-learning workflow is first established to predict the pairwise segregation energy to construct the DS segregation energy spectra that intrinsically include both types of solute-solute interactions. The resulting spectral information is then used to determine the upper and lower bounds of segregation for individual solute species. When applied to magnesium-based ternary systems constructed by alloying Mg with any two of the 11 candidate solute species (Ag, Al, Ca, Co, Cu, Gd, Nd, Ni, Pb, Pd, and Zn), the extended DS segregation framework is successfully validated by hybrid molecular dynamics/Monte Carlo simulations and experimental results available in the literature. Furthermore, we introduce a design strategy to promote co-segregation by incorporating additional solute species that exhibit attractive interactions with existing solutes, thereby enabling enhanced co-segregation even in the presence of strong site competition. These results underscore the critical role of solute-solute interactions in governing co-segregation behavior and provide a predictive pathway for the design and optimization of alloys.

cond-mat.mtrl-sci↗

arXiv2026

Rapid modeling of segregation-driven metal-oxide adhesion in high-entropy alloys using macroscopic atom model

Accurate prediction of metal-oxide adhesion in high-entropy alloys (HEAs) is challenging because interfacial segregation, atomic environments, and macroscopic thermodynamic quantities are strongly correlated. Relying solely on first-principles approaches is too expensive for exploring composition, solute concentration, and co-segregation effects. To address this, we extend the macroscopic atom model (MAM) for multicomponent alloys using composition-consistent surface fractions and an interfacial pair-probability formalism that captures deviations from random contact statistics. Applied to CoCrFeNi (AlCoCrFeNi) HEA in contact with Cr2O3 (Al2O3), the model predicts segregation energies and work of separation as continuous functions of composition, reproducing the correct segregation hierarchy of Hf, Y, Zr, and S. The stronger segregation tendency at Al2O3 interfaces, and the non-linear dependence of surface energy and adhesion on solute content and co-segregation is also captured. The results are benchmarked with DFT calculations, which shows consistent trends, particularly the strengthening of adhesion by Hf and Zr through strong metal-oxygen bonding and the weakening effect of S. These results demonstrate that the extended MAM provides a physically interpretable, computationally efficient, and quantitatively predictive framework for screening segregation-controlled adhesion beyond the limits of DFT.

cond-mat.mtrl-sci↗

arXiv2025

Machine-learned accelerated discovery of oxidation-resistant NiCoCrAl high-entropy alloys

The development of oxidation-resistant high-entropy alloy (HEA) bond coats is restricted by the limited understanding of how multi-principal element interactions govern scale formation across temperatures. This study uncovers new oxidation trends in NiCoCrAl HEAs using a data-driven analysis of high-fidelity experimental oxidation data. The results reveal a clear temperature-dependent transition between alumina- and chromia-dominated protection, identifying the compositional regimes where alloys rich in Al dominate at $\ge1150$ {\deg}C, mixed Al-Cr chemistries are optimal at intermediate temperatures, and, unexpectedly, Cr-rich low-Al alloys perform best at 850 {\deg}C-challenging the assumption that high Al is universally required. The effects of Hf and Y are shown to be strongly composition-dependent with Hf producing the largest global reduction in oxidation rate, while Y becomes effective primarily in NiCo-lean alloys. Y-Hf co-doping offers consistent improvement but exhibits site-saturation behavior. These insights identify new high-performing HEA bond-coat families, including $\mathrm{Ni_{17}Co_{23}Cr_{30}Al_{30}}$ as a substitute for conventional mutlilayer thermal barrier coatings.

cond-mat.mtrl-sci↗

arXiv2025

Effective Mass of a Migrating Interface

Interfaces are ubiquitous in materials and play a central role in microstructural evolution and material properties. Although interface migration has been studied for more than a century and remains an active field, several foundational assumptions of interface kinetics remain largely untested. In particular, interfaces are commonly treated as massless objects governed by overdamped dynamics. In this study, we show that grain boundaries exhibit measurable inertial behavior under high-frequency oscillatory driving. We introduce a quantitative method to extract an effective interface mass from the phase lag between the applied force and the interface velocity, and find that this mass scales with the atoms participating in boundary migration. Using this framework, we identify regimes in which inertial effects significantly modify interfacial kinetics, especially at frequencies relevant to thermal fluctuations. These results challenge the conventional overdamped description and establish effective interface mass as a key ingredient in a physically complete theory of interface migration.

cond-mat.mtrl-sci↗

arXiv2025

Disconnection formation via segregation-induced grain boundary phase transitions

Disconnections, long recognized as the key mediators of grain boundary (GB) kinetics in polycrystalline materials, have traditionally been understood to nucleate through thermal or mechanical activation. In this work, using atomistic simulations, we reveal a distinct nucleation mechanism driven exclusively by solute interstitial segregation across multiple substitutional binary alloy systems (e.g., Al-Ni, Al-Fe). This process exhibits zero-nucleation energy barriers, contrasting sharply with the nucleation mechanisms in pure systems. We identify states that are activated through segregation-induced GB phase transitions: (i) isolated disconnections or phase junctions that promote GB migration and disappear with continuous segregation, and (ii) composite disconnections that are formed via two oppositely oriented isolated disconnections. The disconnections are mechanically robust, suppressing shear-coupled migration and instead resulting in GB amorphization and pure sliding under applied shear loading. The long-range stress fields associated with these composite disconnections further attract solute atoms and assist the nucleation of precipitates. These disconnections, absent in pure materials, follow unique nucleation pathways as confirmed through dichromatic pattern analysis and persist across different alloy chemistries and crystal structures. Our findings demonstrate that solute interstitial segregation provides a powerful and previously unrecognized pathway for barrier-free disconnection formation, thereby fundamentally extending current understanding of GB kinetics in alloy systems.

cond-mat.mtrl-sci↗

arXiv2025

Grain boundary interstitial segregation in substitutional binary alloys

Grain boundary (GB) segregation is a powerful approach for optimizing the thermal and mechanical properties of metal alloys. In this study, we report significant GB interstitial segregation in a representative substitutional binary alloy system (Al-Ni) through atomistic simulations, challenging prevailing assumptions in the literature. Our findings show that Ni atoms preferentially segregate to interstitial sites within numerous kite-like GB structures in the Al bicrystals. An intriguing interplanar interstitial segregation pattern was also observed and analyzed. Additionally, interstitial segregation can induce unexpected GB transitions, such as kite transitions and nano-faceting, due to the existence of small interstitial sites. Building upon these observations, we developed a robust method to systematically identify the interstitial candidate sites for accommodating solutes at GBs. This approach combines site detection with structural filtering to produce distributions of interstitial sites that closely match atomistic simulation results. Applied to nanocrystalline alloys, this method enabled the calculation of interstitial segregation energies, significantly improving GB segregation predictions for the Al-Ni system. Furthermore, machine learning models using smooth overlap of atomic positions descriptors successfully predicted per-site interstitial segregation energy. This study highlights the critical role of GB interstitial segregation in advancing our understanding of solute behavior and provides valuable insights for designing next-generation alloys.

cond-mat.mtrl-sci↗

arXiv2024

Intrinsic grain boundary mobility tensor from three-dimensional interface random walk

In recent years, studies have demonstrated that the grain boundary (GB) migration is a three-dimensional (3D) process, characterized by a 3D mobility tensor. In this study, we develop a 3D interface random walk theory to extract the GB mobility and shear coupling tensors at equilibrium state based on the random walk of the GB position. Using this approach, we mathematically prove the symmetry of the GB mobility tensor in the case of overdamped GB migration. The theory and its conclusions align with molecular dynamics simulation results and disconnection analysis, and the extracted GB mobility and shear coupling tensors reflect the intrinsic GB properties, unaffected by the large driving forces in atomistic simulations. Additionally, we refined the fast adapted random walk (FAIRWalk) method, enabling efficient extraction of the GB mobility tensor from fewer simulations while maintaining high accuracy. Building on this advancement, we conducted an extensive survey of the mobility, shear coupling, and activation energy for the migration of 388 coincidence-site lattice Ni GBs in the Olmsted database. Several intriguing phenomena were observed, including temperature-induced sudden emergence, disappearance, or inversion of shear coupling; GBs with "zero" normal mobility but high shear mobility; and a non-linear relationship between activation energy and mobility. These findings challenge the traditional understanding of GB migration and warrant further investigation.

cond-mat.mtrl-sci↗

arXiv2024

DAPL: Integration of Positive and Negative Descriptions in Text-Based Person Search

Text-based person search (TBPS) aims to retrieve specific images of individuals from large datasets using textual descriptions. Existing TBPS methods focus primarily on identifying explicit positive attributes, often neglecting the critical role of negative descriptions. This oversight can lead to false positives, where images that should be excluded based on negative descriptions are incorrectly included, due to partial alignment with the positive criteria. To address this limitation, we propose the Dual Attribute Prompt Learning (DAPL) framework, which incorporates both positive and negative descriptions to improve the interpretative accuracy of vision-language models in TBPS tasks. DAPL combines Dual Image-Attribute Contrastive (DIAC) learning with Sensitive Image-Attribute Matching (SIAM) learning to enhance the detection of previously unseen attributes. Furthermore, to achieve a balance between coarse and fine-grained alignment of visual and textual embeddings, we introduce the Dynamic Token-wise Similarity (DTS) loss. This loss function refines the representation of both matching and non-matching descriptions at the token level, providing more precise and adaptable similarity assessments, and ultimately improving the accuracy of the matching process. Empirical results demonstrate that DAPL outperforms state-of-the-art methods, enhancing both precision and robustness in TBPS tasks.

cs.CV↗

arXiv2024

Disruptive Atomic Jumps Induce Grain Boundary Stagnation

Grain growth in polycrystalline materials can be impeded by grain boundary (GB) stagnation. Using atomistic simulations, we observed that during GB migration, the disruptive jumps of a few GB atoms can disturb the original ordered collective movement of GB atoms, leading to the stagnation of the entire GB. These disruptive atomic jumps can be activated by both high driving forces and high temperatures, with even jumps of a few atoms capable of causing the stagnation of an entire GB. This mechanism also explains the non-Arrhenius behavior observed in some GBs. Additionally, a large model size could increase the rate of disruptive atomic jumps, and a clear transition in thermal behavior is observed with the increase of the GB size in GBs exhibiting clear thermally activated stagnation. Our further investigation shows that the disruptive atoms involved in these jumps do not differ from other GB atoms in terms of atomic energy, volume, density, local entropy, or Voronoi tessellation, and no "jam transition" was observed in the energy barrier spectra. This fact makes those disruptive jumps challenging to detect. To address this issue, we propose a displacement vector analysis method that effectively identifies these subtle disruptive jumps.

cond-mat.mtrl-sci↗

arXiv2024

Grain boundary segregation prediction with a dual-solute model

Solute segregation along grain boundaries (GBs) profoundly affects their thermodynamic and kinetic behaviors in polycrystalline materials. Recently, the spectral approach has emerged as a powerful tool to predict GB segregation. However, previous GB segregation predictions using this method relied heavily on single-solute segregation energy spectrum without solute-solute interactions, which were often incorporated through a fitting parameter. In this work, we developed a dual-solute model whose segregation energy spectrum intrinsically incorporates solute-solute interactions. It was first validated for GB segregation prediction in the Al-Mg system and then extended to several other distinct binary alloy systems. The dual-solute model shows significant improvement over the single-solute model and can accurately predict the real segregation states obtained by hybrid Molecular Dynamics/Monte Carlo simulations within a broad temperature range with different solute concentrations before forming secondary phases. This dual-solute model provides an effective method for accurately predicting GB segregation in nanocrystalline metals.

cond-mat.mtrl-sci↗

arXiv2024

Unusual acceleration and size effects in grain boundary migration with shear coupling

Grain boundary (GB) migration plays a crucial role in the thermal and mechanical responses of polycrystalline materials, particularly in ultrafine-grained and nano-grained materials exhibiting grain size-dependent properties. This study investigates the migration behaviors of a set of GBs in Ni through atomistic simulations, employing synthetic driving forces and shear stress. Surprisingly, the displacements of some shear-coupling GBs do not follow the widely assumed linear or approximately linear relation with time; instead, they exhibit a noticeable acceleration tendency. Furthermore, as the bicrystal size perpendicular to the GB plane increases, the boundary velocity significantly decreases. These observations are independent of the magnitude and type of driving force but are closely linked to temperature, unique to shear-coupling GBs that display a rise in the kinetic energy component along the shear direction. By adopting a specific boundary condition, the acceleration in migration and size effect can be largely alleviated. However, the continuous rise in kinetic energy persists, leading to the true driving force for GB migration being lower than the applied value. To address this, we propose a technique to extract the true driving force based on a quantitative analysis of the work-energy relation in the bicrystal system. The calculated true mobility reveals that the recently proposed mobility tensor may not be symmetric at relatively large driving forces. These discoveries advance our understanding of GB migration and offer a scheme to extract the true mobility, crucial for meso- and continuum-scale simulations of GB migration-related phenomena such as crack propagation, recrystallization, and grain growth.

cond-mat.mtrl-sci↗

arXiv2023

Intrinsic Grain Boundary Shear Coupling Tensor

Grain boundary (GB) migration stands as a linchpin process governing microstructural evolution in polycrystalline materials. Over the past decade, the concept of shear coupling, quantified through the shear coupling factor, has transformed our understanding and driven the development of theoretical frameworks for unifying GB behaviors. In this study, we introduced a novel concept of shear coupling strength designed to overcome the limitations of the conventional shear coupling factor, notably its deficiency in conveying "coupling" information. The shear coupling tensor formed by the shear coupling strengths characterizes intrinsic shear coupling properties across diverse GBs and reveals complex dynamics within the GB mobility tensor. The molecular dynamics simulation confirms the symmetry of the GB mobility tensor. This symmetry is inherently built into the shear coupling strength, aligning with an assumption made in previous studies. Additionally, an efficient methodology has been developed for streamlined extraction of both shear coupling and GB mobility tensors from atomistic simulations. This advancement holds the potential to sample GB behavior across extensive datasets, significantly enhancing our ability to predict structure-property relationships within the expansive 5-parameter space of GBs.

cond-mat.mtrl-sci↗

arXiv2022

Computing the intrinsic grain boundary mobility tensor

Grain boundary (GB) mobility has been conventionally computed as a single value; however, a recent study has suggested that GB mobility should be expressed as a tensor. In this work, by using atomistic simulations, the concept of GB mobility being applied to the shear direction was re-examined and it is found that it follows the same physical rule as the conventionally defined GB mobility based on the normal direction. The interface random walk method was then used to compute the intrinsic GB mobility tensor at the zero-driving force limit. In order to compute the off-diagonal elements of the intrinsic GB mobility tensor, a shear coupling strength S is introduced in this study, which we believe can better reflect the intrinsic characteristics of a GB for its coupling trend between the normal and shear motion than the widely used shear coupling factor. Furthermore, the effect of temperature and external driving force on the GB mobility tensor, especially on its symmetry, was systematically investigated. It is shown that the GB mobility in either the normal or shear direction can show a non-Arrhenius type dependence on the applied driving force, which is similar to the widely reported non-Arrhenius (or anti-thermal) dependence of GB mobility on temperature. Accordingly, the classical GB migration equation was adapted to describe the diverse variation of GB mobility due to changes in both temperature and driving force.

cond-mat.mtrl-sci↗

arXiv2022

Driving force induced transition in thermal behavior of grain boundary migration in Ni

Grain boundary (GB) migration exhibits intriguing anti-thermal behavior (or non-Arrhenius behavior), with the temperature and driving force playing crucial roles. Through atomistic simulations on nickel bicrystals, we investigate the change in GB mobility with variations in both temperature and driving force. Our results reveal that the GB mobility initially increases with temperature and subsequently decreases after reaching the transition temperature (Ttrans), and, notably, Ttrans exhibits a linear relationship with the activation energy (Q) associated with GB migration. By modulating the driving force, we found that the driving force could effectively lower Q, resulting in the shift of Ttrans towards lower temperatures. Additionally, higher driving forces were found to activate more migration modes at lower temperatures, potentially leading to a transition in the thermal behavior of GB migration. Our work supports the existing theoretical models for GB migration based on both classical thermal activation and disconnection nucleation. Furthermore, we refined the existing model by incorporating the influence of the driving force. The modified model can not only describe the effect of driving force on the thermal behavior of GB migration but also accounts for the observed "anti-driving force" phenomenon in GB migration. Our research has the potential to offer valuable insights for investigating realistic GB migration under more intricate constraints and environments.

cond-mat.mtrl-sci↗