Anomalous electrical conductivity in rapidly crystallized Cu50−x{}_{50-x}Zrx{}_{x} (x = 50 - 66.6) alloys

Cu${}_{50-x}$Zr${}_{x}$ (x = 50, 54, 60 and 66.6) polycrystalline alloys were prepared by arc-melting. The crystal structure of the ingots has been examined by X-ray diffraction. Non-equilibrium martensitic phases with monoclinic structure were detected in all the alloys except Cu${}_{33.4}$Zr${}_{66.6}$. Temperature dependencies of electrical resistivity in the temperature range of T = 4 - 300 K have been measured as well as room temperature values of Hall coefficients and thermal conductivity. Electrical resistivity demonstrates anomalous behavior. At the temperatures lower than 20 K, their temperature dependencies are non-monotonous with pronounced minima. At elevated temperatures they have sufficiently non-linear character which cannot be described within framework of the standard Bloch--Gr\"{u}neisen model. We propose generalized Bloch--Gr\"{u}neisen model with variable Debye temperature which describes experimental resistivity dependencies with high accuracy. We found that both the electrical resistivity and the Hall coefficients reveal metallic-type conductivity in the Cu-Zr alloys. The estimated values of both the charge carrier mobility and the phonon contribution to thermal and electric conductivity indicate the strong lattice defects and structure disorder.Comment: 6 pages, 3 figure

Deep Machine Learning Potentials for Multicomponent Metallic Melts: Development, Predictability and Compositional Transferability

The use of machine learning interatomic potentials (MLIPs) in simulations of materials is a state-of-the-art approach, which allows achieving nearly ab initio accuracy with orders of magnitude less computational cost. Multicomponent disordered systems have a highly complicated potential energy surface due to both topological and compositional disorder. That arises issues in MLIPs developing, such as optimal design strategy of potentials and their predictability and transferability. Here we address MLIPs for multicomponent metallic melts taking the ternary Al-Cu-Ni ones as a convenient example. We use many-body deep machine learning potentials as implemented in the DeePMD-kit to build MLIP that allows describing both atomic structure and dynamics of the system in the whole composition range. Doing that we consider different sets of neural networks hyperparameters and learning schemes to create an optimal MLIP, which allows archiving good accuracy in comparison with both ab initio and experimental data. We find that developed MLIP demonstrates good compositional transferability, which extends far beyond compositional fluctuations in the training configurations. The results obtained open up prospects for simulating structural and dynamical properties of multicomponent metallic alloys with MLIPs. © 2021 Elsevier B.V.This work was supported by the Russian Science Foundation (grant 18–12-00438). Processing of experimental data was supported by the RSF grant 19-73-20053. The numerical calculations are carried out using computing resources of the federal collective usage center ’Complex for Simulation and Data Processing for Mega-science Facilities’ at NRC ’Kurchatov Institute’ (ckp.nrcki.ru/), supercomputers at Joint Supercomputer Center of Russian Academy of Sciences (www.jscc.ru), HybriLIT heterogeneous computing platform (LIT, JINR) (http://hlit.jinr.ru) and ’Uran’ supercomputer of IMM UB RAS (parallel.uran.ru)

Polytetrahedral short-range order and crystallization stability in supercooled Cu64.5Zr35.5 metallic liquid

Development of reliable interatomic potentials is crucial for theoretical studies of the relationship between chemical composition, structure and observable properties in glass-forming metallic alloys. Due to the ambiguity of potential parametrization procedure, certain crucial properties of the system, such as stability against crystallization or symmetry of the ground state crystal phase, may not be correctly reproduced in computer simulations. Here we address this issue for Cu64.5Zr35.5 alloy described by two modifications of embedded atom model potential, as well as by ab initio molecular dynamics. We observe that, at low supercooling, both models provide very similar liquid structure, which agrees satisfactory with that obtained by ab initio simulations. Hoverer, deeply supercooled liquids demonstrate essentially different local structure and thus different stability against crystallization. The system demonstrating more pronounced icosahedral short-range order is more stable against crystallization, which is in agreement with Frank's hypothesis. © 2019 Elsevier B.V.The authors gratefully acknowledge M. Mendelev for helpful discussion and providing EAM potentials. This work is supported by the supercomputer of IMM UB RAS and computing resources of the Federal collective usage center ”Complex for Simulation and Data Processing for Mega-science Facilities” at NRC Kurchatov Institute. Russian Science Foundation (grant RNF18-12-00438 ). Molecular dynamic simulations have been carried out using ”Uran”

Structure and glass-forming ability of simulated Ni-Zr alloys

Binary Cu-Zr system is a representative bulk glassformer demonstrating high glass forming ability due to pronounced icosahedral local ordering. From the first glance, Ni-Zr system is the most natural object to expect the same behavior because nickel and copper are neighbours in the periodic table and have similar physicochemical properties. However, doing molecular dynamics simulations of $\rm Ni_{\alpha}Zr_{1-\alpha}$ alloys described by embedded atom model potential, we observe different behaviour. We conclude that the Ni-Zr system has the same glass-forming ability as an additive binary Lennard-Jones mixture without any chemical interaction. The structural analysis reveals that icosahedral ordering in Ni-Zr alloys is much less pronounced than that in the Cu-Zr ones. We suggest that lack of icosahedral ordering due to peculiarities of interatomic interactions is the reason of relatively poor glass-forming ability of Ni-Zr system

Nucleation instability in super-cooled Cu-Zr-Al glass-forming liquids

Special role in computer simulations of supercooled liquid and glasses is played by few general models representing certain classes of real glass-forming systems. Recently, it was shown that one of the most widely used model glassformers -- Kob-Andersen binary Lennard-Jones mixture -- crystalizes in quite lengthy molecular dynamics simulations and, moreover, it is in fact a very poor glassformer at large system sizes. Thus, our understanding of crystallization stability of model glassformers is far from complete due to the fact that relatively small system sizes and short timescales have been considered so far. Here we address this issue for two embedded atom models intensively used last years in numerical studies of Cu-Zr-(Al) bulk metallic glasses. We consider ${\rm Cu_{64.5}Zr_{35.5}}$ and ${\rm Cu_{46}Zr_{46}Al_{8}}$ alloys as those having high glass-forming ability. Exploring their structural evolution at continuous cooling and isothermal annealing, we observe that both systems nucleate in sufficiently lengthy simulations, though ${\rm Cu_{46}Zr_{46}Al_{8}}$ demonstrate order of magnitude higher critical nucleation time. Moreover, ${\rm Cu_{64.5}Zr_{35.5}}$ is actually unstable to crystallization for large system sizes ($N > 20,000$). Both systems crystallize with the formation of tetrahedrally close packed Laves phases of different types. We reveal that structure of both systems in liquid and glassy state contains comparable amount of polytetrahedral clusters. We argue that nucleation instability of simulated ${\rm Cu_{64.5}Zr_{35.5}}$ alloy is due to the fact that its composition is very close to that for stable ${\rm Cu_2 Zr}$ compound with C15 Laves phase structure.Comment: 10 pages, 9 figure