First-principles study of 4d solute diffusion in nickel

Lomaev, I. L.; Novikov, D.L.; Okatov, S. V.; Gornostyrev, Yuri; Burlatsky, S. F.

doi:10.1007/s10853-014-8119-1

Cited by 18 publications

(5 citation statements)

References 20 publications

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“…This correlation would not be clear if the impurity diffusion barriers were plotted directly as calculated, without referencing to the host. This trend is also observed in the literature that impurity atoms larger than the host atom diffuse faster than expected [4,18,19] and in many cases faster than the host self-diffusivity. This effect can result from both an increased binding between the large impurity atom and vacancies in the host due to misfit strain, as well as a decrease in the impurity-vacancy migration barrier.…”

Section: : Methods 21: Datasetsupporting

confidence: 84%

Robust FCC solute diffusion predictions from ab-initio machine learning methods

Wu¹,

Lorenson²,

Anderson³

et al. 2017

Computational Materials Science

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We evaluate the performance of four machine learning methods for modeling and predicting FCC solute diffusion barriers. More than 200 FCC solute diffusion barriers from previous density functional theory (DFT) calculations served as our dataset to train four machine learning methods: linear regression (LR), decision tree (DT), Gaussian kernel ridge regression (GKRR), and artificial neural network (ANN). We separately optimize key physical descriptors favored by each method to model diffusion barriers. We also assess the ability of each method to extrapolate when faced with new hosts with limited known data. GKRR and ANN were found to perform the best, showing 0.15 eV cross-validation errors and predicting impurity diffusion in new hosts to within 0.2 eV when given only 5 data points from the host. We demonstrate the success of a combined DFT + data mining approach towards solving materials science challenges and predict the diffusion barrier of all available impurities across all FCC hosts.Keywords: Diffusion; Data-mining; Machine learning; DFT; Neural network 1: Introduction Atomic migration in solids governs the kinetics of many materials processes, including precipitation, high-temperature creep, phase transformation, and solution homogenization. A particular class of atomic diffusion is dilute impurity diffusion, which refers to the diffusion of a dilute solute in a host. Such diffusion is relevant in many materials applications as dilute solutes are common due to either undesired impurities or intentional dopants in materials. Due to its importance to materials science, large experimental catalogues of impurity diffusion measurements have been collected [1,2], and more recently, first-principles predictions of dilute impurity diffusion coefficients have been conducted [3][4][5][6][7][8]. However, both experimental and theoretical approaches are limited by several drawbacks. Experimental diffusivities often vary significantly due to uncertainties introduced by different measurement techniques and other impurity effects in sample materials. In addition, experiments require significant diffusion kinetics to achieve good data, which generally limits them to be relatively high temperature (e.g., above about 50% of the melting temperature of the host) [9]. Finally, experiments are time consuming and expensive relative to first-principles calculations, as they require significant equipment and human interaction. First-principles calculations are an increasingly powerful tool for predicting dilute impurity diffusion, and compared to experiments can be done at a tiny fraction of the cost of equipment and human time. Furthermore, first-principles predicted energies are expected to be most accurate at lower temperatures, where vibrational and electronic excitations play a minor role, which suggests that these methods may have their best accuracy in temperature domains complimentary to experiments. First-principles methods bring with them significant approximations in both the fundamental energetics and in trea...

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Section: : Methods 21: Datasetsupporting

confidence: 84%

Robust FCC solute diffusion predictions from ab-initio machine learning methods

Wu¹,

Lorenson²,

Anderson³

et al. 2017

Computational Materials Science

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show abstract

“…This is encouraging, as the migration barrier convergence is within the acceptable convergence level of previous similar diffusion studies. [2,5,36,37] The activation energies, Q, show a level of consistency equivalent with the results for DE f and DE m .…”

Section: Results At 0 Ksupporting

confidence: 74%

A Systematic First-Principles Study of Computational Parameters Affecting Self-diffusion Coefficients in FCC Ag, Cu, and Ni

Hargather

O’Connell

2022

J. Phase Equilib. Diffus.

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The diffusion coefficient of a metallic alloy system is critical to understanding phase transformations, mechanical properties, and failure mechanisms such as creep. Rather than performing costly and time consuming experiments, first-principles calculations based on density functional theory have emerged as a robust alternative to experimentally determined diffusion coefficients. In this work, first-principles calculations based on density functional theory are used to investigate the effects of varying the exchange-correlation functional and supercell size had on the accuracy of calculated self-diffusion coefficients. Self-diffusion and thermodynamic properties of Cu, Ni, and Ag are calculated using the two exchange-correlation functionals: the local density approximation and the generalized gradient approximation of Perdew, Burke, and Ernzerhof for solids. Supercells of 32, 64, and 108 atoms are used to investigate the minimum supercell size necessary to isolate the vacancy. Finally, two methods for accounting for vibrational entropy contributions due to temperature are employed: a simple estimation of the diffusion prefactor used previously in the literature and harmonic phonon calculations. The estimation of the diffusion prefactor produces acceptable results for Cu, but not for the other metals investigated. The harmonic phonon calculations improves both self-diffusion and thermodynamic properties when compared to experimental values for Cu, Ni, and Ag. For Cu and Ni, self-diffusion coefficients and related thermodynamic properties are consistent and stable regardless of the supercell size or exchange-correlation functional employed.

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“…The effect is different from the bonding of sp solute which lowers the migration barrier, as noted by Lomaev et al [64]. It was suggested by the same authors that the electronic effect plays a more dominant role in diffusion of either sp elements in transition metal matrix or transition metal elements in sp-matrix, whereas the diffusion of sp/transition element in sp/transition metal matrix is controlled by the size effect [64,85]. Additional exploration of chemical bond analysis would be valuable to unravel the similarities and disparities among various groups of solute elements, but further discussion of this topic is beyond the scope of this work.…”

Section: Phosphorus Effect On Vacancy Migration Barriersmentioning

confidence: 93%

Ab initio study of phosphorus effect on vacancy-mediated process in nickel alloys – An insight into Ni2Cr ordering

Young

Tucker

2019

Acta Materialia

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The development of long range order in nickel-chromium alloys is of great technological interest but the kinetics and mechanisms of the transformation are poorly understood. The present research utilizes a combined computational and experimental approach to elucidate the mechanism by which phosphorus accelerates the ordering rate of stoichiometric Ni 2 Cr in Ni-Cr alloys. A series of Ni-33%Cr-x%P samples (in atomic percent) were fabricated with phosphorus concentrations, x = <0.005-0.1 at.% and aged between 373 and 470°C for times up to 3000 h.The first-principles modeling considers fcc Ni with dilute P as a reasonable approximation for the complex Ni-Cr-P alloy system. Calculation results show a pronounced enhancement of vacancy transport by vacancy-solute pair diffusion via consecutive exchange and rotation jumps of vacancies associated with the phosphorus atom. The energy barriers of these two migration paths are at least 0.35 eV lower than that of vacancy-atom exchange in pure Ni solvent. The analytical diffusion model predicts enhanced solvent diffusion by 2 orders of magnitude for 0.1 at.% P at 400-500°C. The model prediction is in good agreement with the evolution of micro-hardness.We characterize the micro-hardness result by a kinetic ordering model, showing a significant decrease of the activation energy of ordering transformation. These results help gauge the risk of industrial alloys developing long range order which increases strength but degrades ductility and toughness. Specifically, minor alloying additions that bind with excess vacancies and lower the vacancy migration barrier can greatly accelerate hardening via Ni 2 Cr precipitation.

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First-principles study of 4d solute diffusion in nickel

Cited by 18 publications

References 20 publications

Robust FCC solute diffusion predictions from ab-initio machine learning methods

Robust FCC solute diffusion predictions from ab-initio machine learning methods

A Systematic First-Principles Study of Computational Parameters Affecting Self-diffusion Coefficients in FCC Ag, Cu, and Ni

Ab initio study of phosphorus effect on vacancy-mediated process in nickel alloys – An insight into Ni2Cr ordering

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