Ab-initio based modeling of diffusion in dilute bcc Fe–Ni and Fe–Cr alloys and implications for radiation induced segregation

Choudhury, Samrat; Barnard, Leland; Tucker, Julie D.; Allen, Todd R.; Wirth, Brian D.; Asta, Mark; Morgan, Dane

doi:10.1016/j.jnucmat.2010.12.231

Cited by 111 publications

(108 citation statements)

References 71 publications

(130 reference statements)

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“…Especially for self-diffusion and for impurity diffusion in dilute alloys, generally good agreement has been found with experimental data [1][2][3][4][5][6][7][8]. For point defects in pure metals, generally excellent agreement with high temperature experimental data can be achieved provided that sufficient thermal excitation effects are included [9][10][11].…”

Section: Introductionmentioning

confidence: 61%

Ab initioprediction of vacancy properties in concentrated alloys: The case of fcc Cu-Ni

Zhang

Sluiter

2015

Phys. Rev. B

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Vacancy properties in concentrated alloys continue to be of great interest because nowadays ab initio supercell simulations reach a scale where even defect properties in disordered alloys appear to be within reach. We show that vacancy properties cannot generally be extracted from supercell total energies in a consistent manner without a statistical model. Essential features of such a model are knowledge of the chemical potential and imposition of invariants. In the present work, we derive the simplest model that satisfies these requirements and we compare it with models in the literature. As illustration we compute ab initio vacancy properties of fcc Cu-Ni alloys as a function of composition and temperature. Ab initio density functional calculations were performed for SQS supercells at various compositions with and without vacancies. Various methods of extracting alloy vacancy properties were examined. A ternary cluster expansion yielded effective cluster interactions (ECIs) for the Cu-Ni-Vac system. Composition and temperature dependent alloy vacancy concentrations were obtained using statistical thermodynamic models with the ab initio ECIs. An Arrhenius analysis showed that the heat of vacancy formation was well represented by a linear function of temperature. The positive slope of the temperature dependence implies a negative configurational entropy contribution to the vacancy formation free energy in the alloy. These findings can be understood by considering local coordination effects.

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Section: Introductionmentioning

confidence: 61%

Ab initioprediction of vacancy properties in concentrated alloys: The case of fcc Cu-Ni

Zhang

Sluiter

2015

Phys. Rev. B

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“…[9][10][11][12][13][14] However, for concentrated solid-solution alloys, the chemical disorder makes it difficult to calculate the defect energies. Usually, there are two methods that can be used to address this problem.…”

Section: Introductionmentioning

confidence: 99%

Defect energetics of concentrated solid-solution alloys from ab initio calculations: Ni_0.5Co_0.5, Ni_0.5Fe_0.5, Ni_0.8Fe_0.2and Ni_0.8Cr_0.2

Zhao

Stocks

Zhang

2016

Phys. Chem. Chem. Phys.

156

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It has been shown that concentrated solid solution alloys possess unusual electronic, magnetic, transport, mechanical and radiation-resistant properties that are directly related to underlying chemical complexity. Because every atom experiences a different local atomic environment, the formation and migration energies of vacancies and interstitials in these alloys exhibit a distribution, rather than a single value as in a pure metal or dilute alloy. Using ab initio calculations based on density functional theory and special quasirandom structure, we have characterized the distribution of defect formation energy and migration barrier in four Ni-based solid-solution alloys: Ni 0.5 Co 0.5 , Ni 0.5 Fe 0.5 , Ni 0.8 Fe 0.2 , and Ni 0.8 Cr 0.2. As defect formation energies in finite-size models depend sensitively on the elemental chemical potential, we have developed a computationally efficient method for determining it which takes into account the global composition and the local short-range order. In addition we have compared the results of our ab initio calculations to those obtained from available embedded atom method (EAM) potentials. Our results indicate that the defect formation and migration energies are closely related to the specific atomic size in the structure, which further determines the elemental diffusion properties. Different EAM potentials yield different features of defect energetics in concentrated alloys, pointing to the need for additional potential development efforts in order to allow spatial and temporal scale-up of defect and simulations, beyond those accessible to ab initio methods.2

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“…4 Further, the approach described here provides a basis also for extending ML algorithms to non-equilibrium defect properties such as those related to diffusion in alloys. 18 As mentioned above, we demonstrate the ML strategy for defect types in this work by focusing on the modeling of dominant intrinsic point defect types in intermetallic compounds displaying the simple cubic B2 crystal structure illustrated in Fig. 1.…”

Section: Introductionmentioning

confidence: 94%

Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

et al. 2016

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We present a combination of machine learning and high throughput calculations to predict the points defects behavior in binary intermetallic (A-B) compounds, using as an example systems with the cubic B2 crystal structure (with equiatomic AB stoichiometry). To the best of our knowledge, this work is the first application of machine learning-models for point defect properties. High throughput first principles density functional calculations have been employed to compute intrinsic point defect energies in 100 B2 intermetallic compounds. The systems are classified into two groups: (i) those for which the intrinsic defects are antisites for both A and B rich compositions, and (ii) those for which vacancies are the dominant defect for either or both composition ranges. The data was analyzed by machine learning-techniques using decision tree, and full and reduced multiple additive regression tree (MART) models. Among these three schemes, a reduced MART (r-MART) model using six descriptors (formation energy, minimum and difference of electron densities at the Wigner-Seitz cell boundary, atomic radius difference, maximal atomic number and maximal electronegativity) presents the highest fit (98 %) and predictive (75 %) accuracy. This model is used to predict the defect behavior of other B2 compounds, and it is found that 45 % of the compounds considered feature vacancies as dominant defects for either A or B rich compositions (or both). The ability to predict dominant defect types is important for the modeling of thermodynamic and kinetic properties of intermetallic compounds, and the present results illustrate how this information can be derived using modern tools combining high throughput calculations and data analytics.

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Ab-initio based modeling of diffusion in dilute bcc Fe–Ni and Fe–Cr alloys and implications for radiation induced segregation

Cited by 111 publications

References 71 publications

Ab initioprediction of vacancy properties in concentrated alloys: The case of fcc Cu-Ni

Ab initioprediction of vacancy properties in concentrated alloys: The case of fcc Cu-Ni

Defect energetics of concentrated solid-solution alloys from ab initio calculations: Ni_0.5Co_0.5, Ni_0.5Fe_0.5, Ni_0.8Fe_0.2and Ni_0.8Cr_0.2

Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

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Ab-initio based modeling of diffusion in dilute bcc Fe–Ni and Fe–Cr alloys and implications for radiation induced segregation

Cited by 111 publications

References 71 publications

Ab initioprediction of vacancy properties in concentrated alloys: The case of fcc Cu-Ni

Ab initioprediction of vacancy properties in concentrated alloys: The case of fcc Cu-Ni

Defect energetics of concentrated solid-solution alloys from ab initio calculations: Ni0.5Co0.5, Ni0.5Fe0.5, Ni0.8Fe0.2and Ni0.8Cr0.2

Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

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Defect energetics of concentrated solid-solution alloys from ab initio calculations: Ni_0.5Co_0.5, Ni_0.5Fe_0.5, Ni_0.8Fe_0.2and Ni_0.8Cr_0.2