We develop a fast and accurate machine-learned interatomic potential for the Mo-Nb-Ta-V-W quinary system and use it to study segregation and defects in the body-centred cubic refractory high-entropy alloy MoNbTaVW. In the bulk alloy, we observe clear ordering of mainly Mo-Ta and V-W binaries at low temperatures. In damaged crystals, our simulations reveal clear segregation of vanadium, the smallest atom in the alloy, to compressed interstitial-rich regions like radiation-induced dislocation loops. Vanadium also dominates the population of single self-interstitial atoms. In contrast, due to its larger size and low surface energy, niobium segregates to spacious regions like the inner surfaces of voids. When annealing samples with supersaturated concentrations of defects, we find that in complete contrast to W, interstitial atoms in MoNbTaVW cluster to create only small ($\sim 1$ nm) experimentally invisible dislocation loops enriched by vanadium. By comparison to W, we explain this by the reduced but three-dimensional migration of interstitials, the immobility of dislocation loops, and the increased mobility of vacancies in the high-entropy alloy, which together promote defect recombination over clustering.
Chemical segregation and structural transitions at interfaces are important nanoscale phenomena, making them natural targets for atomistic modeling, yet interatomic potentials must be fit to secondary physical properties. To isolate the important factors that interatomic potentials must capture in order to accurately model such behavior, the performance of four interatomic potentials was evaluated for the Cu-Zr system, with experimental observations used to provide validation. While experimental results show strong Zr segregation to grain boundary regions and the formation of nanoscale amorpho…
To enable accurate molecular dynamics simulations of iron-chromium alloys with surfaces, we develop, based on density-functional-theory (DFT) calculations, a new interatomic Fe-Cr potential in the Tersoff formalism. Contrary to previous potential models, which have been designed for bulk Fe-Cr, we extend our potential fitting database to include not only conventional bulk properties but also surface-segregation energies of Cr in bcc Fe. In terms of reproducing our DFT results for the bulk properties, the new potential is found to be superior to the previously developed Tersoff potential and co…
From electronic-structure-based thermodynamic linear-response, we establish chemical ordering behavior in complex solid solutions versus how Gibbs' space is traversed -- applying it on prototype refractory A2 Ta-Nb-Mo-W high-entropy alloys. Near ideal stoichiometry, this alloy has anomalous, intricate chemical ordering tendencies, with long-ranged chemical interactions that produce competing short-range order (SRO) with a crossover to spinodal segregation. This atypical SRO arises from canonical band behavior that, with alloying, create features near the Fermi-surface (well-defined even with d…
New approaches for the design of alloy systems with multiprincipal elements is recently researched in refractory materials field. However, most research aimed at arc melting process with weakness of coarsening of grains and inhomogeneous microstructure of segregation of elements during the cooling. This study aims to design and fabricate high-entropy alloy with powder metallurgy. In this study, a refractory high entropy alloys with composition near AlMo0.5NbTa0.5TiZr were produced by powder metallurgy. The alloy consists of two body-centered cubic (BCC) phases. One phase was disordered BCC enr…
Machine learning potential as a guide for eutectic in ultra-refractory multicomponent ceramics
Moscow Institute of Physics and Technology (National Research University)（莫斯科物理科学与技术大学）；Institute for High Pressure Physics, Russian Academy of Sciences（俄罗斯科学院高压物理研究所）；Joint Institute for Nuclear Research（苏联联合核研究所）
Interfacial segregation and chemical short-range ordering influence the behavior of grain boundaries in complex concentrated alloys. In this study, we use atomistic modeling of a NbMoTaW refractory complex concentrated alloy to provide insight into the interplay between these two phenomena. Hybrid Monte Carlo and molecular dynamics simulations are performed on columnar grain models to identify equilibrium grain boundary structures. Our results reveal extended near-boundary segregation zones that are much larger than traditional segregation regions, which also exhibit chemical patterning that b…
Nanocrystalline alloys (nanoalloys) are prone to grain growth. It is known that grain boundary segregation and precipitation can stabilize nanoalloys, but the stabilization becomes less effective at high temperatures and adding grain growth inhibitors often reduces sinterability. Herein, we have simultaneously achieved improved sinterability and exceptional high-temperature stability for a class of MoNbTaTiW-based refractory high-entropy nanoalloys (RHENs). Bulk pellets of RHENs were fabricated through planetary ball milling and spark plasma sintering, achieving 93-96% relative densities with …
Compositionally complex alloys or concentrated solid solutions are the latest frontier in catalyst design, but mixing different elements in one catalyst may result in surface segregation. Atomistic simulations can predict segregation patterns, but standard approaches based on mean-field models, cluster expansion, or classical interatomic potentials are often limited for the description of multicomponent alloys. We present machine learning potentials that can describe surface segregation with near DFT accuracy. The method is used to study a complex Co-Cu-Fe-Mo-Ni quinary alloy. For this alloy, …
Effect of cold rolling strain on the microstructural evolution in equimolar MoNbTaTiZr refractory complex concentrated alloy: Comprehensive characterization
Interstitials as a key ingredient for P segregation to grain boundaries in polycrystalline $α$-Fe
Montanuniversität Leoben（维也纳矿业大学）；Christian Doppler Laboratory for Knowledge-based Design of Advanced Steels, Department of Materials Science, Montanuniversität Leoben（维也纳矿业大学材料科学系先进钢知识基础设计克里斯蒂安·道普勒实验室）
Refractory multi-principal element alloys exhibiting promising mechanical properties such as excellent strength retention at elevated temperatures have been attracting increasing attention. Although their inherent chemical complexity is considered a defining feature, a challenge arises in predicting local chemical ordering, particularly in grain boundary regions with enhanced structural disorder. In this study, we use atomistic simulations of a large group of bicrystal models to sample a wide variety of interfacial sites (grain boundary) in NbMoTaW and explore emergent trends in interfacial se…
Cracking the case: fluctuations enhance ductility in refractory alloys
École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Laboratory of materials design and simulation (MADES), Institute of Materials, École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院材料研究所材料设计与模拟实验室）；National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院新型材料计算设计与发现国家中心）；EMPA - Swiss Federal Laboratories for Materials Science and Technology（瑞士联邦材料科学与技术研究院）；Laboratory for Multiscale Modeling in Mechanics (LAMMM), Institute of Mechanical Engineering, École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院机械工程学院多尺度力学建模实验室）；School of Engineering, Brown University, Providence, RI 02906 USA（布朗大学工程学院）
Owing to the excellent catalysis properties of Ag-Au binary nanoalloy, nanostructured Ag-Au, such as Ag-Au nanoparticles and nanopillars, have been under intense investigation. To achieve high accuracy in molecular simulations of the Ag-Au nanoalloys, the surface properties are required to be modeled with first-principles precision. In this work, we propose a generalizable machine-learning interatomic potential for the Ag-Au nanoalloys based on deep neural networks, trained from a database constructed with the first-principle calculations. This potential is highlighted by the accurate predicti…
Refractory multi-principal element alloys (MPEAs) have exceptional mechanical properties, including high strength-to-weight ratio and fracture toughness, at high temperatures. Here, we elucidate the complex interplay between segregation, short range order and strengthening in the NbMoTaW MPEA through atomistic simulations with a highly accurate machine learning interatomic potential. In the single crystal MPEA, we find greatly reduced anisotropy in the critically resolved shear stress between screw and edge dislocations compared to the elemental metals. In the polycrystalline MPEA, we demonstr…
Hydrogen segregation around a straight screw dislocation in bcc iron
Laboratoire des Sciences de l’Ingénieur pour l’Environnement (LaSIE) - UMR CNRS 7356（环境工程科学实验室）；Laboratoire des Sciences des Procédés et des Matériaux (LSPM) - UPR 3407（工艺与材料科学实验室）
Segregation, precipitation, and phase separation in Fe-Cr systems is investigated. Monte Carlo simulations using semiempirical interatomic potential, first-principles total energy calculations, and experimental spectroscopy are used. In order to obtain a general picture of the relation of the atomic interactions and properties of Fe-Cr alloys in bulk, surface, and interface regions several complementary methods has to be used. Using Exact Muffin-Tin Orbitals method the effective chemical potential as a function of Cr content (0-15 at.% Cr) is calculated for a surface, second atomic layer and b…
Bond-order potentials (BOPs) are derived from the tight-binding (TB) approximation and provide a linearly-scaling computation of the energy and forces for a system of interacting atoms. While the numerical BOPs involve the numerical integration of the response (Green's) function, the expressions for the energy and interatomic forces are analytical within the formalism of the analytic BOPs. In this paper we present a detailed comparison of numerical and analytic BOPs. We use established parametrisations for the bcc refractory metals W and Mo and test structural energy differences; tetragonal, t…
Due to the vast compositional space of multi-principal element alloys (MPEAs), the rational design of MPEAs for optimized microstructures is difficult. Therefore, a high-throughput first-principles study of Mo-V-Nb-Ti-Zr, a refractory MPEA, was conducted to gain insights into the underlying microstructures. Using Monte-Carlo simulations powered by cluster expansion, we uncover the principles governing the MPEA's microstructures across a large compositional space that includes non-equiatomic compositions and encompasses the constituent binaries, ternaries, and quaternaries. In the spirit of Hum…
Ab initio simulations of dislocations are essential to build quantitative models of material strength, but the required system sizes are often at or beyond the limit of existing methods. Many important structures are thus missing in the training or validation of interatomic potentials, whilst studies of dislocation-defect interactions must mitigate the effect of strong periodic image interactions along the line direction. We show how these restrictions can be lifted through the use of linear machine learning potentials in hybrid simulations, where only a subset of atoms are governed by ab init…
Grain Growth Kinetics in (Cr,Mo,Ta,V,W)C1-δ High-Entropy Carbide Ceramics
Missouri University of Science and Technology（密苏里科技大学）；Pennsylvania State University（宾夕法尼亚州立大学）；Duke University（杜克大学）；University of California, Irvine（加州大学尔湾分校）
