arXiv subjects

Liang Qi

Publications and source records attributed to Liang Qi.

At least 19 recordsLinked to original sources

arXiv2026

The influence of Y content on grain structure evolution in Mg-Y alloys

To advance the understanding of microstructural evolution behavior in Mg-rare earth alloys, the effect of yttrium (Y) addition on static recrystallization and grain growth in Mg alloys was systematically investigated in extruded Mg-1wt.%Y and Mg-7wt.%Y alloys. Y addition was found to significantly retard the microstructural evolution, primarily due to its solute drag effect arising from Y segregation at grain boundaries. The relative intensity of solute drag effects from different alloying elements in Mg alloys was further assessed from both thermodynamic and kinetic perspectives, considering their grain boundary segregation tendencies and diffusivities. Additionally, static recrystallization in Mg-Y alloys was observed to proceed via a two-stage behavior characterized with two distinct JMAK exponents, indicating the heterogeneous nucleation of recrystallized grains. Abnormal grain growth (AGG) behavior was observed in these Mg-Y alloys. Overall, this study highlights the critical role of Y segregation at grain boundaries in controlling recrystallization and grain growth kinetics in Mg-Y alloys. This provides new insights into the design of thermally stable Mg alloys with refined microstructures.

cond-mat.mtrl-sci↗

arXiv2026

Strain-Rate- and Line-Length-Dependent Screw Dislocation Glide Mechanisms in BCC Refractory Metals and Alloys

Plastic flow in body-centered cubic (BCC) metals and dilute/concentrated alloys is governed by the motion of <111> screw dislocations, whose glide is often impeded by cross-kinks (jogs). While existing strengthening models typically treat depinning as defect-assisted cutting or dislocation bowing, the combined strain-rate and dislocation-line-length dependence of cross-kink stability and effective obstacle spacing remains insufficiently resolved at the atomistic scale. Here, we combine conventional molecular dynamics and strain-boost hyperdynamics to investigate screw-dislocation glide in pure Nb and Mo, dilute Nb-Mo alloys, and equiatomic NbMo at 300 K over strain rates from 10^3 to 10^7 s^-1 and dislocation line lengths from 15 to 50 nm. We first demonstrate that low-strain-rate simulations require sufficiently long dislocation lines to capture consistent cross-kink behavior and strength-determining pinning events. Using the 50~nm configurations, we show that cross-kinks form not only in concentrated alloys but also in pure BCC metals, with their stability governed by the relative rates of kink nucleation and migration on primary and cross-slip planes, which differ between Nb- and Mo-rich systems due to distinct core structures and non-Schmid responses. At high strain rates, depinning proceeds predominantly via vacancy-interstitial cluster formation. In contrast, at low strain rates and long line lengths, alternative pathways emerge, including lateral cross-kink migration, three-dimensional forward--backward cross-slip, and prismatic loop formation. The effective obstacle spacing controlling the critical resolved shear stress therefore emerges from coupled thermodynamic roughening and kinetic evolution. These findings highlight the intrinsically rate-, length-, and chemistry-dependent nature of screw-dislocation strengthening in BCC alloys.

cond-mat.mtrl-sci↗

arXiv2025

Visualizing metal-mediated nucleation and growth of GaN

Understanding the atomic-scale mechanisms governing metal-mediated nucleation and growth of gallium nitride (GaN) and related alloys is critical for tailoring their structural and functional properties in advanced electronic, optoelectronic, and quantum devices. Using real-time environmental transmission electron microscopy (E-TEM) in conjunction with Gibbs free energy calculations, we elucidate the distinct processes of GaN nucleation and growth from Ga droplet arrays with and without GaN pre-nuclei. For the lowest temperatures, although GaN nucleation at Ga droplet arrays is not observed, GaN growth occurs preferentially at pre-existing GaN nuclei, presumably due to the reduced Gibbs free energy for NH3 decomposition at Ga/GaN interfaces. For intermediate to high temperatures, E-TEM reveals nucleation and growth of GaN from Ga droplets with and without GaN nuclei, with enhanced crystallinity for the GaN nuclei, due to epitaxial templating. These results highlight the critical role of the Ga/GaN interface in facilitating NH3 decomposition and GaN growth, offering fundamental insights into metal-mediated nucleation and growth of GaN and related materials.

cond-mat.mtrl-sci↗

arXiv2025

Energetic Origins of Competing Deformation Modes in Metastable Titanium Alloys

Metastable alloys, such as $\beta$-phase titanium (Ti) alloys with a body-centered cubic (BCC) lattice, can exhibit exceptional mechanical properties through the interplay of multiple deformation mechanisms -- diffusionless phase transformations, deformation twinning, and conventional dislocation slip. However, understanding how these mechanisms compete or cooperate across a wide range of metastable alloys and loading conditions remains a fundamental challenge. Here, we employ molecular dynamics (MD) simulations to investigate the nucleation behavior of competing deformation modes in metastable $\beta$-Ti alloys as a function of temperature, composition, and loading conditions. We reveal that twinning pathways emerge through reversible transformations between the $\beta$ phase and the orthorhombic $\alpha"$ phase, in agreement with crystallographic theories. Quantitative analyses demonstrate that the dominant deformation mechanisms and preferred twinning-plane orientations are governed by two key energetic parameters: the free energy barrier for homogeneous $\beta \leftrightarrow \alpha"$ transformations and the misfit strain energy along specific phase boundaries. These energetic quantities vary systematically with thermodynamic and mechanical conditions, thereby rationalizing the deformation mode transitions observed in both simulations and experiments. These energetic metrics offer a physically grounded and computationally tractable basis for designing next-generation metastable alloys.

cond-mat.mtrl-sci↗

arXiv2025

The role of the solid-melt interface in accelerating the self-catalyzed growth kinetics of III-V semiconductors

Solid-melt interfaces play a pivotal role in governing crystal growth and metal-mediated epitaxy of gallium nitride (GaN) and other semiconductor materials. Using atomistic simulations based on machine-learning interatomic potentials (MLIPs), we uncover that multiple layers of Ga atoms at the GaN-Ga melt interface form structurally ordered and electronically charged configurations that are critical for the growth kinetics of GaN. These ordered layers modulate the free energy landscape (FEL) for N adsorption and substantially reduce the migration barriers for N at the interface compared to a clean GaN surface. Leveraging these interfacial energetics, kinetic Monte Carlo (KMC) simulations reveal that GaN growth follows a diffusion-controlled, layer-by-layer mechanism, with the FEL for N adsorption emerging as the rate-limiting factor. By incorporating facet-specific FELs and the diffusivity/solubility of N in Ga melt, we develop a predictive, fitting-free transport model that estimates facet-dependent growth rates in the range of ~0.01 to 0.04 nm/s, in agreement with experimental growth rates observed in GaN nanoparticles synthesized by Ga-mediated molecular beam epitaxy (MBE). This multiscale framework offers a generalizable and quantitative approach to link atomic-scale ordering and interfacial energetics to macroscopic phenomena, providing actionable insights for the rational design of metal-mediated epitaxial processes.

cond-mat.mtrl-sci↗

arXiv2025

Multiscale Modeling of Vacancy-Cluster Interactions and Solute Clustering Kinetics in Multicomponent Alloys

Prediction of solute clustering kinetics in aged multicomponent alloys requires a quantitative understanding of complex vacancy-cluster interactions across multiple scales. Here, we develop an integrated computational framework combining on-lattice kinetic Monte Carlo (KMC) simulations, absorbing Markov chain models, and mesoscale cluster dynamics (CD) to investigate these interactions in Al-Mg-Zn alloys. The Markov chain model yields vacancy escape times from solute clusters and identifies a two-stage behavior of the vacancy-cluster binding energy. These binding energies are used to estimate residual vacancy concentrations in the Al matrix after quenching, which serve as critical inputs to CD simulations to predict long-term cluster evolution kinetics during natural aging. Our results quantitatively demonstrate the significant impact of quench rate on natural aging kinetics. Results provide insights to guide alloy chemistry, quench rates, and aging time at finite temperature to control the evolution of solute clusters and eventual precipitates in aged multicomponent alloys.

cond-mat.mtrl-sci↗

arXiv2023

Thermodynamic, Kinetic and Mechanical Modeling to Evaluate CO2-induced Corrosion via Oxidation and Carburization in Fe, Ni alloys

A computational framework integrating thermodynamics, kinetics, and mechanical stress calculations is developed to study supercritical CO2 induced corrosion in model Fe-based MA956 and Ni-based H214 alloys. Empirical models parametrized using experimental data show surface oxidation and sub-surface carburization for a wide range of thermodynamic conditions (800-1200 {\deg}C, 1-250 bar). CALPHAD simulations based on empirical models demonstrate higher carburization resistance in H214 compared to MA956 below 900 {\deg}C and through-thickness carburization in both alloys at higher temperatures. Finite element modeling reveals enhanced volumetric misfit induced stresses at oxide and carbide interfaces, and its critical dependence on the carbide chemistry and concentration.

cond-mat.mtrl-sci↗

arXiv2022

Mechanism of Local Lattice Distortion Effects on Vacancy Migration Barriers in FCC Alloys

Accurate prediction of vacancy migration energy barriers, $ΔE_a$, in multi-component alloys is extremely challenging yet critical for the development of diffusional transformation kinetics needed to model alloy behavior in many technological applications. Here, results from $ΔE_a$ and the energy driving force $ΔE$ of many (>1000) vacancy migration events calculated using density functional theory and nudged elastic band method show large changes (~1eV) of $ΔE_a$ in different local chemical environments of the model face-centered cubic Al-Mg-Zn alloys. Due to local lattice distortion effects induced by solute atoms (such as Mg) with different sizes than the matrix element (Al), the changes of $ΔE_a$ for one type of migrating atoms originate primarily from fluctuations of $Δe_a\equiv ΔE_a - \frac{1}{2}ΔE$. To understand the fluctuations, a quartic function is shown to accurately describe the energy landscape of the minimum energy path (MEP) for each vacancy migration event. Analyses of the quartic function show that $Δe_a$ can be approximated with $Δe_a \approx αk_fD^2$, where $α\sim 0.022$ is a constant of all types of migrating atoms. Here $D$ is the distance of a migrating atom between two adjacent equilibrium positions and $k_f$ is the average vibration spring constant of this atom at these two equilibrium positions. $k_f$ and $D$ quantitatively describe the lattice distortion effects on the curvatures and locations of the MEP at its initial and final states in different local chemical environments. We also used the local lattice occupations as inputs to train surrogate models to predict coefficients of the quartic function, which accurately and efficiently output both $ΔE_a$ and $ΔE$ as the necessary inputs for the mesoscale studies of diffusional transformation in Al-Mg-Zn alloys.

cond-mat.mtrl-sci↗

arXiv2022

A bond counting model for accurate prediction of lattice parameter of bcc solid solution alloys

Lattice Parameter is an important material feature in High Entropy Alloy (HEA) Design. Vegards Law is typically used to estimate lattice parameters but is often inaccurate for metal alloys due to an inability to account for charge transfer which can affect atomic volumes. The present study used ab initio simulation to calculate bond lengths between atoms of dissimilar elements in B2 intermetallic compounds which was then combined with a bond counting model to produce a model to estimate the lattice parameters of Refractory BCC HEAS. The model was tested using a supercell method which modeled various random solid solution HEAs. The proposed model produced lattice parameters with superior accuracy to Vegards Law without the need for large DFT calculations or fitting parameters. The proposed model had a root mean squared error (RMSE) of 0.006 Angstroms which is half that of Vegards Law RMSE (0.012 Angstrom).

cond-mat.mtrl-sci↗

arXiv2022

Multi-scale Investigation of Chemical Short-Range Order and Dislocation Glide in the MoNbTi and TaNbTi Refractory Multi-Principal Element Alloys

Refractory multi-principal element alloys (RMPEAs) are promising materials for high-temperature structural applications. Here, we investigate the role of chemical short-range ordering (CSRO) on dislocation glide in two model RMPEAs - TaNbTi and MoNbTi - using a multi-scale modeling approach. A highly accurate machine learning interatomic potential was developed for the Mo-Ta-Nb-Ti system and used to demonstrate that MoNbTi exhibits a much greater degree of SRO than TaNbTi and the local composition has a direct effect on the unstable stacking fault energies (USFE). From mesoscale phase-field dislocation dynamics simulations, we find that increasing SRO leads to higher mean USFEs, thereby increasing the stress required for dislocation glide. The gliding dislocations experience significant hardening due to pinning and depinning caused by random compositional fluctuations, with higher SRO decreasing the degree of USFE dispersion and hence, amount of hardening. Finally, we show how the morphology of an expanding dislocation loop is affected by the applied stress, with higher SRO requiring higher applied stresses to achieve smooth screw dislocation glide.

cond-mat.mtrl-sci↗

arXiv2020

MU-GAN: Facial Attribute Editing based on Multi-attention Mechanism

Facial attribute editing has mainly two objectives: 1) translating image from a source domain to a target one, and 2) only changing the facial regions related to a target attribute and preserving the attribute-excluding details. In this work, we propose a Multi-attention U-Net-based Generative Adversarial Network (MU-GAN). First, we replace a classic convolutional encoder-decoder with a symmetric U-Net-like structure in a generator, and then apply an additive attention mechanism to build attention-based U-Net connections for adaptively transferring encoder representations to complement a decoder with attribute-excluding detail and enhance attribute editing ability. Second, a self-attention mechanism is incorporated into convolutional layers for modeling long-range and multi-level dependencies across image regions. experimental results indicate that our method is capable of balancing attribute editing ability and details preservation ability, and can decouple the correlation among attributes. It outperforms the state-of-the-art methods in terms of attribute manipulation accuracy and image quality.

cs.CV↗

arXiv2020

Screening of generalized stacking fault energies, surface energies and intrinsic ductile potency of refractory multicomponent alloys

Body-centered cubic (bcc) refractory multicomponent alloys are of great interest due to their remarkable strength at high temperatures. Meanwhile, further optimizing the chemical compositions of these alloys to achieve a combination of high strength and room-temperature ductility remains challenging, which would require systematic predictions of the correlated alloy properties across a vast compositional space. In the present work, we performed first-principles calculations with the special quasi-random structure (SQS) method to predict the unstable stacking fault energy ($γ_{usf}$) of the $(1\bar10)[111]$ slip system and the $(1\bar10)$-plane surface energy ($γ_{surf}$) for 106 individual binary, ternary and quaternary bcc solid-solution alloys with constituent elements among Ti, Zr, Hf, V, Nb, Ta, Mo, W, Re and Ru. Moreover, with the first-principles data and a set of physics-informed descriptors, we developed surrogate models based on statistical regression to accurately and efficiently predict $γ_{usf}$ and $γ_{surf}$ for refractory multicomponent alloys in the 10-element compositional space. Building upon binary and ternary data, the surrogate models show outstanding predictive ability in the high-order multicomponent systems. The ratio between $γ_{surf}$ and $γ_{usf}$ is a parameter to reflect the potency of intrinsic ductility of an alloy based on the Rice model of crack-tip deformation. Therefore, using the surrogate models, we performed a systematic screening of $γ_{usf}$, $γ_{surf}$ and their ratio over 112,378 alloy compositions to search for alloy candidates that may have enhanced strength-ductile synergies. Search results were also confirmed by additional first-principles calculations.

cond-mat.mtrl-sci↗

arXiv2019

Predicting densities and elastic moduli of SiO2-based glasses by machine learning

Chemical design of SiO2-based glasses with high elastic moduli and low weight is of great interest. However, it is difficult to find a universal expression to predict the elastic moduli according to the glass composition before synthesis since the elastic moduli are a complex function of interatomic bonds and their ordering at different length scales. Here we show that the densities and elastic moduli of SiO2-based glasses can be efficiently predicted by machine learning (ML) techniques across a complex compositional space with multiple (>10) types of additive oxides besides SiO2. Our machine learning approach relies on a training set generated by high-throughput molecular dynamic (MD) simulations, a set of elaborately constructed descriptors that bridges the empirical statistical modeling with the fundamental physics of interatomic bonding, and a statistical learning/predicting model developed by implementing least absolute shrinkage and selection operator with a gradient boost machine (GBM-LASSO). The predictions of the ML model are comprehensively compared and validated with a large amount of both simulation and experimental data. By just training with a dataset only composed of binary and ternary glass samples, our model shows very promising capabilities to predict the density and elastic moduli for k-nary SiO2-based glasses beyond the training set. As an example of its potential applications, our GBM-LASSO model was used to perform a rapid and low-cost screening of many (~105) compositions of a multicomponent glass system to construct a compositional-property database that allows for a fruitful overview on the glass density and elastic properties.

cond-mat.mtrl-sci↗

arXiv2019

Electron Localization Enhances Cation Diffusion in Transition Metal Oxides: An Electronic Trebuchet Effect

Ion diffusion is a central part of materials physics of fabrication, deformation, phase transformation, structure stability and electrochemical devices. Conventional theory focuses on the defects that mediate diffusion and explains how their populations influenced by oxidation, reduction, irradiation and doping can enhance diffusion. However, we have found the same influences can also elevate their mobility by orders of magnitude in several prototypical transition-metal oxides. First-principles calculation fundamentally connects the latter observation to migrating ion's local structure, which is inherently soft and has a broken symmetry, making it susceptible to electron or hole localization, thereby realizing a lower saddle-point energy. This finding resolves an unanswered question in physical ceramics of the past 30 years: why cation diffusion against the prediction of classical nonstoichiometric defect physics is enhanced in reduced zirconia, ceria and structurally related ceramics? It also suggests the saddle-point electron-phonon interaction that enables a negative-U state is akin to the counterweight effect that enables a trebuchet. This simple picture for the transitional state explains why enhanced kinetics mediated by radical-like-ion migration occurs often, especially under extreme conditions.

cond-mat.mtrl-sci↗

arXiv2019

Universal correlation between electronic factors and solute-defect interactions in bcc refractory metals

The interactions between solute atoms and crystalline defects such as vacancies, dislocations, and grain boundaries play an essential role in determining physical, chemical and mechanical properties of solid-solution alloys. Here we present a universal correlation between two electronic factors and the solute-defect interaction energies in binary alloys of body-centered-cubic (bcc) refractory metals (such as W and Ta) with transition-metal substitutional solutes. One electronic factor is the bimodality of the d-orbital local density of states for a matrix atom at the substitutional site, and the other is related to the hybridization strength between the valance sp- and d-bands for the same matrix atom. Remarkably, the correlation is independent of the types of defects and the locations of substitutional sites, following a linear relation for a particular pair of solute-matrix elements. Our findings provide a novel and quantitative guidance to engineer the solute-defect interactions in alloys based on electronic structures.

cond-mat.mtrl-sci↗

arXiv2018

Electron Localization Enhances Cation Diffusion in Reduced ZrO2, CeO2 and BaTiO3

According to defect chemistry, the experimental observations of enhanced cation diffusion in a reducing atmosphere in zirconia, ceria and barium titanate are in support of an interstitial mechanism. Yet previous computational studies always found a much higher formation energy for cation interstitials than for cation vacancies, which would rule out the interstitial mechanism. The conundrum has been resolved via first-principles calculations comparing migration of reduced cations and oxidized ones, in cubic ZrO2, CeO2 and BaTiO3. In nearly all cases, reduction alone lowers the migration barrier, and pronounced lowering results if cation's electrostatic energy at the saddle point decreases. The latter is most effectively realized when a Ti cation is allowed to migrate via an empty Ba site thus being fully screened all the way by neighboring anions. Since reduction creates oxygen vacancies as well, which are highly mobile, we also studied their effect on cation migration, and found it only marginally lowers the migration barrier. In several cases, however, a large synergistic effect between cation reduction and oxygen vacancy is revealed, causing an electron to localize in the saddle-point state at a much lower energy than normal, signaling that the saddle point is a negative-U state in which the soft environment enables a large electron-phonon interaction that can over-compensate the on-site Coulomb repulsion. These general findings are expected to be applicable to defect-mediated ion migration in most transitional metal oxides.

cond-mat.mtrl-sci↗

arXiv2017

A Computational Study of Yttria-Stabilized Zirconia: I. Using Crystal Chemistry to Search for the Ground State on a Glassy Energy Landscape

Yttria-stabilized zirconia (YSZ), a ZrO2-Y2O3 solid solution that contains a large population of oxygen vacancies, is widely used in energy and industrial applications. Past computational studies correctly predicted the anion diffusivity but not the cation diffusivity, which is important for material processing and stability. One of the challenges lies in identifying a plausible configuration akin to the ground state in a glassy landscape. This is unlikely to come from random sampling of even a very large sample space, but the odds are much improved by incorporating packing preferences revealed by a modest sized configurational library established from empirical potential calculations. Ab initio calculations corroborated these preferences, which prove remarkably robust extending to the fifth cation-oxygen shell about 8 Å away. Yet because of frustration there are still rampant violations of packing preferences and charge neutrality in the ground state, and the approach toward it bears a close analogy to glass relaxations. Fast relaxations proceed by fast oxygen movement around cations, while slow relaxations require slow cation diffusion. The latter is necessarily cooperative because of strong coupling imposed by the long-range packing preferences.

cond-mat.mtrl-sci↗

arXiv2017

A Computational Study of Yttria-Stabilized Zirconia: II. Cation Diffusion

Cubic yttria-stabilized zirconia is widely used in industrial electrochemical devices. While its fast oxygen ion diffusion is well understood, why cation diffusion is much slower-its activation energy (~5 eV) is 10 times that of anion diffusion-remains a mystery. Indeed, all previous computational studies predicted more than 5 eV is needed for forming a cation defect, and another 5 eV for moving one. In contrast, our ab initio calculations have correctly predicted the experimentally observed cation diffusivity. We found Schottky pairs are the dominant defects that provide cation vacancies, and their local environments and migrating path are dictated by packing preferences. As a cation exchanges position with a neighboring vacancy, it passes by an empty interstitial site and severely displaces two oxygen neighbors with shortened Zr-O distances. This causes a short-range repulsion against the migrating cation and a long-range disturbance of the surrounding, which explains why cation diffusion is relatively difficult. In comparison, cubic zirconia's migrating oxygen only minimally disturbs neighboring Zr, which explains why it is a fast oxygen conductor.

cond-mat.mtrl-sci↗