2005
DOI: 10.1016/j.actamat.2005.01.044
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Diffusion rates of 3d transition metal solutes in nickel by first-principles calculations

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Cited by 97 publications
(75 citation statements)
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“…This can be attributed to the work of Janotti et al [31] using the ultrasoft pseudopotential method [15], because the PAW [16] method used in the present work reconstructs the exact valence wave function with smaller core radii than the ultrasoft potentials, making the PAW a little more computationally expensive, but also more accurate. The previous studies on solute diffusion in Ni showed that there was no direct correlation between the activation energy barrier for diffusion and size of the impurity element [2,31], results that are reproduced in the present work. The present work also shows that in general, 5d elements consistently have higher activation energies than the corresponding 4d element in the same row, while that trend does not hold for 3d elements.…”
Section: Diffusion Coefficient Calculationsupporting
confidence: 82%
See 1 more Smart Citation
“…This can be attributed to the work of Janotti et al [31] using the ultrasoft pseudopotential method [15], because the PAW [16] method used in the present work reconstructs the exact valence wave function with smaller core radii than the ultrasoft potentials, making the PAW a little more computationally expensive, but also more accurate. The previous studies on solute diffusion in Ni showed that there was no direct correlation between the activation energy barrier for diffusion and size of the impurity element [2,31], results that are reproduced in the present work. The present work also shows that in general, 5d elements consistently have higher activation energies than the corresponding 4d element in the same row, while that trend does not hold for 3d elements.…”
Section: Diffusion Coefficient Calculationsupporting
confidence: 82%
“…For the sake of simplicity and efficiency, D 0 can be estimated by treating the vibrational degrees of freedom near the saddle point as nearly simple harmonic oscillators [32]. This value was found to be on the order of 10 -5 m 2 /s for the dilute diffusion of transition metals in Ni [2,31]. Thus, 10 -5 m 2 /s will be used for the diffusion prefactor in this work as an approximation, a value that well represents the experimental diffusion prefactor of pure Ni (8.5×10 -5 -17.7×10 -5 m 2 /s [33][34][35]).…”
Section: Diffusion Coefficient Calculationmentioning
confidence: 99%
“…Similar to the transition metals, 27 atomic size plays a minor role in the vacancy-mediated diffusion of poor metals in α-Ti. Comparing the diffusivities obtained, the largest atom (In, with atomic radius of 1.54 Å) is the fastest diffuser amongst the poor metals, while Si is the slowest diffusing poor metal solute in Ti, although it has a relatively small atomic radius (1.11 Å).…”
Section: A Atomic Size and Solute Diffusionmentioning
confidence: 99%
“…This trend was reported by Ding et al [60] and Messina et al [61] also. It has been found for transition-metal (TM) impurities diffusion in fcc Ni as well [53]. The inverse relation between the magnitude of H mig,2 and atomic size has been rationalized through the displacement of the large impurity atom toward the vacancy.…”
Section: B Migration Barriersmentioning
confidence: 96%
“…This is particularly the case for diffusion in iron where an Arrhenius plot does not show a simple linear relation in the ferromagnetic state. Density functional theory (DFT) calculations have proven successful in predicting experimental data such as lattice parameters [46,47], elastic properties [48,49], and energy barriers for diffusion, e.g., diffusivities in aluminum [50], magnesium [51,52], and nickel [53]. Many impurity diffusivities in bcc iron [54][55][56][57][58][59][60][61] have been calculated with DFT methods, but oftentimes only experimentally fitted data, such as activation energy for diffusion, have been compared with the computed results.…”
Section: Introductionmentioning
confidence: 99%