A molecular dynamics simulation of the diffusion of the solute (Au) and the self-diffusion of the solvent (Cu) in a very dilute liquid Cu–Au solution

Szpunar, Barbara; Smith, R. W.

doi:10.1088/0953-8984/22/3/035105

Cited by 8 publications

(10 citation statements)

References 10 publications

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“…self-, tracer and chemical diffusivities in both the liquid and Fcc_A1 phases are compared with the reported experimental data. Figure 2(a) presents the presently calculated self-diffusivities of Al, Cu, Fe, Mn, and Zn in the liquid state along with the experimental data [38][39][40][41][42][43] and theoretically predicted data [44][45][46][47][48][49][50][51][52][53][54][55]. In Fig.…”

Section: Verification and Validation Of The Databasementioning

confidence: 99%

“…(a) (b) Figure 2 (a) Calculated self-diffusivities of Al, Cu, Fe, Mn and Zn in the liquid state compared with experimental data [38][39][40][41][42][43] and theoretical predicted data [44][45][46][47][48][49][50][51][52][53][54][55]; (b) calculated tracer diffusivity of Cu in Al-Cu, Ag-Cu melts, and tracer diffusivity of Ni in Al-Ni and Ni-Si melts along with experimental data [56][57][58][59][60]. A constant, M, has been added to separate the data.…”

Section: Diffusion Foundations Vol 15mentioning

confidence: 99%

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Kinetic Simulations of Diffusion-Controlled Phase Transformations in Cu-Based Alloys

Tang

Chen

Engström

2018

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Abstract. In this chapter, we present kinetic simulations of diffusion-controlled phase transformations in Cu-based alloys by using our most recently developed atomic mobility database (MOBCU) for copper alloys. This database consists of 29 elements including most common ones for industrial copper alloys. It contains descriptions for both the liquid and Fcc_A1 phases. The database was developed through a hybrid CALPHAD approach based on experiments, first-principles calculations, and empirical rules. We demonstrate that by coupling the created mobility database with the existing compatible thermodynamic database (TCCU), all kinds of diffusivities in both solid solution and liquid phases in Cu-based alloys can be readily calculated. Furthermore, we have applied the combination of MOBCU and TCCU to simulate diffusion-controlled phenomena, such as solidification, nucleation, growth, and coarsening of precipitates by using the kinetic modules (DICTRA and TC-PRISMA) in the Thermo-Calc software package. Many examples of simulations for different alloys are shown and compared with experimental observations. The remarkable agreements between calculation and experimental results suggest that the atomic mobilities for Cu-based alloys have been satisfactorily described. This newly developed mobility database is expected to be continuously improved and extended in future and will provide fundamental kinetic data for computer-aided design of copper base alloys.

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Section: Verification and Validation Of The Databasementioning

confidence: 99%

Section: Diffusion Foundations Vol 15mentioning

confidence: 99%

Kinetic Simulations of Diffusion-Controlled Phase Transformations in Cu-Based Alloys

Tang

Chen

Engström

2018

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“…In Figure , a log plot of D S L / d 0 is shown versus T f / T for Cu in different experiments and simulations. The experiments are for Al 90 Cu 10 , Al 83 Cu 17 , Al 75 Cu 25 , Al 80 Cu 20 , and Cu 100 , and the simulations are for Cu 100 and Cu 60 Ti 20 Zr 20 . , All of the data are thus shown to be collapsed onto the master curve given by eq with η = 4/3, where T f , T c , and d 0 are listed in Table . The deviation from the master curve is seen in the simulation results for Cu 60 Ti 20 Zr 20 at lower temperatures around log 10 ( D S L / d 0 ) ≃ −3.5, below which those systems are considered to be out of equilibrium.…”

Section: Analyses Of Fragile Liquidsmentioning

confidence: 99%

Universality in Self-Diffusion of Atoms among Distinctly Different Glass-Forming Liquids

Tokuyama

2011

J. Phys. Chem. B

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The long-time self-diffusion coefficients D(S)(L) in distinctly different glass-forming liquids are analyzed from a unified point of view recently proposed by the present author. It is shown that as long as the systems are in equilibrium, they are all described by the following two types of master curves, depending on whether the control parameter is intensive or extensive: D(S)(L)(x) = d(0)x(-1)(1 - x)(2+η) exp[cx(3+η)(1 - x)(2+η)] for a reduced intensive control parameter x, such as a reduced inverse temperature, and D(S)(L)(x) = d(0)x(-1)(1 - x)(2) for a reduced extensive control parameter x, such as a reduced volume fraction, where d(0) and c are constant. Here, the exponent η (≠0) results from many-body correlations in a supercooled liquid state. The constants η and c depend on the systems and are given by (η,c) = (4/3,62) for fragile liquids, (5/3, 62) for strong liquids, and (0,0) for other glass-forming systems in which the peak heights of their non-Gaussian parameters are always much less than 1.0. It is also shown that all of the data for the diffusion coefficient start to deviate from the master curves at lower temperatures (or higher volume fraction), where the systems become out of equilibrium, leading to a glass state.

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“…Moreover, it has been demonstrated that the same EAM potentials due to solid state can also describe interactions in liquid metals very well, such as Cu, Au, Ag and Ni. The critical component of a realistic molecular dynamic simulation for the motion of atoms is to have a good description of the interatomic potential [76].…”

Section: Simulationmentioning

confidence: 99%

Atomic mobilities and diffusivities in Al alloys

Zhang

Cui

et al. 2011

Sci. China Technol. Sci.

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Knowledge of diffusivity is a prerequisite for understanding many scientific and technological disciplines. In this paper, firstly major experimental methods, which are employed to provide various diffusivity data, are briefly described. Secondly, the fundamentals of various computational methods, including first-principles method, embedded atomic method/molecular dynamic simulation, semi-empirical approaches, and phenomenological DICTRA technique, are demonstrated. Diffusion models recently developed for order/disorder transitions and stoichiometric compounds are also briefly depicted. Thirdly, a newly established diffusivity database for liquid, fcc_A1, L1 2 , bcc_A2, bcc_B2, and intermetallic phases in the multicomponent Al alloys is presented via a few case studies in binary, ternary and quaternary systems. And the integration of various computational techniques and experimental methods is highlighted. The reliability of this diffusivity database is validated by comparing the calculated and measured concentration profiles, diffusion paths, and Kirkendall shifts in various binary, ternary and quaternary diffusion couples. Next, the established diffusivity databases along with thermodynamic and other thermo-physical properties are utilized to simulate the microstructural evolution for Al alloys during solidification, interdiffusion and precipitation. A special discussion is presented on the phase-field simulation of interdiffusion microstructures in a series of Ni-Al diffusion couples composed of γ, γ', and β phases under the effects of both coherent strain and external compressive force. Future orientations in the establishment of next generation of diffusivity database are finally addressed.

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A molecular dynamics simulation of the diffusion of the solute (Au) and the self-diffusion of the solvent (Cu) in a very dilute liquid Cu–Au solution

Cited by 8 publications

References 10 publications

Kinetic Simulations of Diffusion-Controlled Phase Transformations in Cu-Based Alloys

Kinetic Simulations of Diffusion-Controlled Phase Transformations in Cu-Based Alloys

Universality in Self-Diffusion of Atoms among Distinctly Different Glass-Forming Liquids

Atomic mobilities and diffusivities in Al alloys

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