arXiv subjects

Chongze Hu

Publications and source records attributed to Chongze Hu.

12 recordsLinked to original sources

arXiv2026

Ab initio Monte Carlo prediction of order-to-disorder transitions in multicomponent MXenes

This letter predicts unprecedented order-to-disorder transition behaviors in multicomponent MXenes using an integrated and improved first-principles Monte Carlo (MC) framework. The improvements include (i) structural relaxation and (ii) selective atom swapping during MC iterations for more accurate and efficient predictions. Using (TiMo)-based double transition metal (DTM) carbide MXenes as a model system, ab initio MC simulations reveal that surface termination and coordination environments play critical roles in governing chemical ordering in MXenes. Specifically, the formation of out-of-plane MXene (o-MXenes) with Mo segregation to outermost metallic layers (M') is only driven by the oxygen (O) termination at prismatic sites. In contrast, O termination at octahedral sites and fluorine (F) termination at both prismatic and octahedral sites always promote the formation of o-MXenes with Ti-segregated to M' layers. Furthermore, changing the F/O ratio at prismatic termination sites or alternating the atomic coordination within the MXene lattices can induce an order-to-disorder transition in DTM MXenes.

cond-mat.mtrl-sci↗

arXiv2025

A Panoramic View of MXenes via a New Design Strategy

Two-dimensional (2D) transition metal carbides and nitrides, known as MXenes, possess unique physical and chemical properties, enabling diverse applications in fields ranging from energy storage to communication, catalysis, sensing, healthcare, and beyond. The transition metal and nonmetallic atoms in MXenes can exhibit distinct coordination environments, potentially leading to a wide variety of 2D phases. Despite extensive research and significant advancements, a fundamental understanding of MXenes' phase diversity and its relationship with their hierarchical precursors, including intermediate MAX phases and parent bulk phases, remains limited. Using high-throughput modeling based on first-principles density functional theory, we unveil a wide range of MXenes and comprehensively evaluate their relative stabilities across a large chemical space. The key lies in considering both octahedral and trigonal prismatic coordination environments characteristic of various bulk phases. Through this comprehensive structural library of MXenes, we uncover a close alignment between the phase stability of MXenes and that of their hierarchical 3D counterparts. Building on this, we demonstrate a new design strategy where the atomic coordination environments in parent bulk phases can serve as reliable predictors for the design of MXenes, reducing reliance on intermediate MAX phases. Our study significantly expands the landscape of MXenes, at least doubling the number of possible structures.

cond-mat.mtrl-sci↗

arXiv2023

Creating Continuously Graded Microstructures with Electric Fields via Locally Altering Grain Boundary Complexions

Tailoring microstructures represents a daunting goal in materials science. Here, an innovative proposition is to utilize grain boundary (GB) complexions (a.k.a. interfacial phases) to manipulate microstructural evolution, which is challenging to control via only temperature and doping. Herein, we use ZnO as a model system to tailor microstructures using applied electric fields as a new knob to control GB structures locally via field-driven stoichiometry (defects) polarization. Specifically, continuously graded microstructures are created under applied electric fields. By employing aberration-corrected scanning transmission electron microscopy (AC STEM) in conjunction with density functional theory (DFT) and ab initio molecular dynamics (AIMD), we discover cation-deficient, oxygen-rich GBs near the anode with enhanced GB diffusivities. In addition, the field-driven redistribution of cation vacancies is deduced from a defect chemistry model, and subsequently verified by spatially resolved photoluminescence spectroscopy. This bulk stoichiometry polarization leads to preferential formation of cation-deficient (oxidized) GBs near the anode to gradually promote grain growth towards the anode. This mechanism can be utilized to create continuously graded microstructures without abnormal grain growth typically observed in prior studies. This study exemplifies a case of tailoring microstructural evolution via altering GB complexions locally with applied electric fields, and it enriches fundamental GB science.

cond-mat.mtrl-sci↗

arXiv2022

Grain Boundary Segregation Transitions and Critical Phenomena in Binary Regular Solutions: A Systematics of Complexion Diagrams with Universal Characters

A systematics of grain boundary (GB) segregation transitions and critical phenomena has been derived to expand the classical GB segregation theory. Using twist GBs as an example, this study uncovers when GB layering vs. prewetting transitions should occur and how they are related to one another. Moreover, a novel descriptor, normalized segregation strength (phi_seg), is introduced. It can represent several factors that control GB segregation, including strain and bond energies, as well as misorientation for small-angle GBs (in a mean-field approximation), which had to be treated separately in prior models. In a strong segregation system with a large phi_seg, first-order layering transitions occur at low temperatures and become continuous above GB roughing temperatures. With reducing phi_seg, the layering transitions gradually merge and finally lump into prewetting transitions without quantized layer numbers, akin to Cahn's critical-point wetting model. Furthermore, GB complexion diagrams with universal characters are constructed as the GB counterpart to the classical exemplar of Pelton-Thompson regular-solution binary bulk phase diagrams.

cond-mat.mtrl-sci↗

arXiv2022

Computing Grain Boundary Diagrams of Thermodynamic and Mechanical Properties

Computing the grain boundary (GB) counterparts to bulk phase diagrams represents an emerging research direction. Using a classical embrittlement model system Ga-doped Al alloy, this study demonstrates the feasibility of computing temperature- and composition-dependent GB diagrams to represent not only equilibrium thermodynamic and structural characters, but also mechanical properties. Specifically, hybrid Monte Carlo and molecular dynamics (MC/MD) simulations are used to obtain the equilibrium GB structure as a function of temperature and composition. Simulated GB structures are validated by aberration-corrected scanning transmission electron microscopy. Subsequently, MD tensile tests are performed on the simulated equilibrium GB structures. GB diagrams are computed for not only GB adsorption and structural disorder, but also interfacial structural and chemical widths, MD ultimate tensile strength, and MD tensile toughness. This study suggests a research direction to investigate GB composition-structure-property relationships via computing GB diagrams of thermodynamic, structural, and mechanical (or potentially other) properties.

cond-mat.mtrl-sci↗

arXiv2021

A New Type of Compositionally Complex M5Si3 Silicides: Cation Ordering and Unexpected Phase Stability

A new type of compositionally complex (medium- or high-entropy) M5Si3 silicides is synthesized. Both (V1/5Cr1/5Nb1/5Ta1/5W1/5)5Si3 and (Ti1/5Zr1/5Nb1/5Mo1/5Hf1/5)5Si3 form single-phase homogenous solid solutions. Notably, (V1/5Cr1/5Nb1/5Ta1/5W1/5)5Si3 forms the hexagonal gamma (D88) phase, while all its five constituent binary silicides, V5Si3, Cr5Si3, Nb5Si3, Ta5Si3, and W5Si3, are stable in the tetragonal alpha (D8l) or beta (D8m) phases. Annealing at 1600C demonstrates that this hexagonal gamma phase is stable. Comparison of the experimental and calculated X-ray diffraction patterns, Rietveld refinements, and analysis of aberration-corrected scanning transmission electron microscopy high-angle annular dark-field images suggest cation ordering, which reduces the configurational entropy. This work expands the field of high-entropy and compositional complex ceramics by not only discovering a new compositional complex silicide phase but also demonstrating the cation ordering and unusual phase stability. These compositionally complex silicides can be combined with refractory high-entropy alloys to make the high-entropy counterparts to the Nb-silicide and Mo-Si-B composites.

cond-mat.mtrl-sci↗

arXiv2021

Decoding Grain Boundary Thermodynamics in High-Entropy Alloys in a 5D Space: Coupled Segregation and Disordering

Grain boundaries (GBs) can critically influence the microstructural evolution and various materials properties. However, a fundamental understanding of GBs in high-entropy alloys (HEAs) is lacking because of the complex couplings of the segregations of multiple elements and interfacial disordering, which can generate new phenomena and challenge the classical theories. Here, by combining large-scale atomistic simulations and machine learning, we demonstrate the feasibility of predicting the GB properties as functions of four independent compositional degrees of freedoms and temperature in a 5D space. Subsequently, GB counterparts to bulk phase diagrams are constructed for the first time for quinary HEAs. A data-driven discovery further reveals new coupled segregation and disordering effects in HEAs. Notably, an analysis of a large dataset discovers a critical compensation temperature at which the segregations of all elements virtually vanish simultaneously. While the machine learning model can predict GB properties via a black-box approach, a surrogate data-based analytical model (DBAM) is constructed to provide more physics insights and better transferability, with good accuracies. This study not only provides a new paradigm enabling prediction of GB properties in a 5D space, but also uncovers new GB segregation phenomena in HEAs beyond the classical GB segregation models.

cond-mat.mtrl-sci↗

arXiv2021

Discovery of Electrochemically Induced Grain Boundary Transitions

Electric fields and currents, which are used in innovative materials processing and electrochemical energy conversion, can often alter microstructures in unexpected ways. However, little is known about the underlying mechanisms. Using ZnO-Bi2O3 as a model system, this study uncovers how an applied electric current can change the microstructural evolution through an electrochemically induced grain boundary (GB) transition. By combining aberration-corrected electron microscopy, photoluminescence spectroscopy, first-principles calculations, a generalizable thermodynamic model, and ab initio molecular dynamics, this study reveals that electrochemical reduction can cause a GB disorder-to-order transition to markedly increase GB diffusivities and mobilities. Consequently, abruptly enhanced or abnormal grain growth takes place. These findings advance our fundamental knowledge of GB complexion (phase-like) transitions and electric field effects on microstructural stability and evolution, with broad scientific and technological impacts. A new method to tailor the GB structures and properties, as well as the microstructures, electrochemically can also be envisioned.

cond-mat.mtrl-sci↗

arXiv2020

Single-Phase Duodenary High-Entropy Fluorite/Pyrochlore Oxides with an Order-Disorder Transition

Improved thermomechanical properties have been reported for various high-entropy oxides containing typically five metal cations. This study further investigates a series of duodenary (11 metals + oxygen) high-entropy oxides by mixing different fractions of a five-cation fluorite-structured niobate and a seven-cation pyrochlore (both containing Yb) with matching lattice constants. Nine compositions of duodenary high-entropy oxides have been examined. All of them exhibit single high-entropy phases of either disordered fluorite or ordered pyrochlore structure. An order-disorder transition (ODT) is evident with changing composition, accompanied by a reduction in thermal conductivity (k). In comparison with the ODT criteria developed from ternary oxides, these duodenary oxides are more prone to disorder, but the ODT is still controlled by similar factors (but at different thresholds). Interestingly, there are abrupt increases in Young's modulus (E) at low mixing concentrations near both endmembers. The E/k ratios are increased, in comparison with both endmembers. This study suggests a new route to tailor high-entropy ceramics via controlling cation ordering vs. disordering.

cond-mat.mtrl-sci↗

arXiv2020

Dual-Phase High-Entropy Ultra-High Temperature Ceramics

A series of dual-phase high-entropy ultrahigh temperature ceramics (DPHE-UHTCs) are fabricated starting from N binary borides and (5-N) binary carbides powders. >~99% relative densities have been achieved with virtually no native oxides. These DPHE-UHTCs consist of a hexagonal high-entropy boride (HEB) phase and a cubic high-entropy carbide (HEC) phase. A thermodynamic relation that governs the compositions of the HEB and HEC phases in equilibrium is discovered and a thermodynamic model is proposed. These DPHE-UHTCs exhibit tunable grain size, Vickers microhardness, Young' and shear moduli, and thermal conductivity. The DPHE-UHTCs have higher hardness than the weighted linear average of the two single-phase HEB and HEC, which are already harder than the rule-of-mixture averages of individual binary borides and carbides. This study extends the state of the art by introducing dual-phase high-entropy ceramics (DPHECs), which provide a new platform to tailor various properties via changing the phase fraction and microstructure.

cond-mat.mtrl-sci↗

arXiv2020

Genetic Algorithm-Guided Deep Learning of Grain Boundary Diagrams: Addressing the Challenge of Five Degrees of Freedom

Grain boundaries (GBs) often control the processing and properties of polycrystalline materials. Here, a potentially transformative research is represented by constructing GB property diagrams as functions of temperature and bulk composition, also called "complexion diagrams," as a general materials science tool on par with phase diagrams. However, a GB has five macroscopic (crystallographic) degrees of freedom (DOFs). It is essentially a "mission impossible" to construct property diagrams for GBs as a function of five DOFs by either experiments or modeling. Herein, we combine isobaric semi-grand-canonical ensemble hybrid Monte Carlo and molecular dynamics (hybrid MC/MD) simulations with a genetic algorithm (GA) and deep neural network (DNN) models to tackle this grand challenge. The DNN prediction is ~108 faster than atomistic simulations, thereby enabling the construction of the property diagrams for millions of distinctly different GBs of five DOFs. Notably, excellent prediction accuracies have been achieved for not only symmetric-tilt and twist GBs, but also asymmetric-tilt and mixed tilt-twist GBs; the latter are more complex and much less understood, but they are ubiquitous and often limit the performance properties of real polycrystals as the weak links. The data-driven prediction of GB properties as function of temperature, bulk composition, and five crystallographic DOFs (i.e., in a 7D space) opens a new paradigm.

cond-mat.mtrl-sci↗

arXiv2018

Quantum-Accurate Spectral Neighbor Analysis Potential Models for Ni-Mo Binary Alloys and FCC Metals

In recent years, efficient inter-atomic potentials approaching the accuracy of density functional theory (DFT) calculations have been developed using rigorous atomic descriptors satisfying strict invariances, for example, to translation, rotation, permutation of homonuclear atoms, among others. In this work, we generalize the spectral neighbor analysis potential (SNAP) model to bcc-fcc binary alloy systems. We demonstrate that machine-learned SNAP models can yield significant improvements even over well-established, high-performing embedded atom method (EAM) and modified EAM (MEAM) potentials for fcc Cu and Ni. We also report on the development of a SNAP model for the fcc Ni-bcc Mo binary system by machine learning a carefully-constructed large computed data set of elemental and intermetallic compounds. We demonstrate that this binary Ni-Mo SNAP model can achieve excellent agreement with experiments in the prediction of Ni-Mo phase diagram as well as near-DFT accuracy in the prediction of many key properties such as elastic constants, formation energies, melting points, etc., across the entire binary composition range. In contrast, the existing Ni-Mo EAM has significant errors in the prediction of the phase diagram and completely fails in binary compounds. This work provides a systematic model development process for multicomponent alloy systems, including an efficient procedure to optimize the hyper-parameters in the model fitting, and paves the way to long-time, large-scale simulations of such systems.

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