OpenIEC: an open-source code for interfacial energy calculation in alloys

Yang, Shenglan; Zhong, Jue; Wang, Jiong; Zhang, Lijun; Kaptay, George

doi:10.1007/s10853-019-03639-w

Cited by 16 publications

(5 citation statements)

References 79 publications

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“…On evaluating the temperature dependence of r predicted by Eq. [23], it is evident that r % 0 at~1800 K, which compares favorably with the predictions of Yang et al [37] and Kaptay [17] that r = 0 at 1893 K. According to Eq. [6] DX e = 0 at 1684 K. Since r = 0 when DX e = 0, Eq.…”

Section: Temperature Dependence Of the Interfacial Free Energysupporting

confidence: 84%

“…The temperature dependence predicted by Eq. [23] appears to agree best with those of Kaptay [17] and Mishin, [36] which are in remarkably good agreement with each other over the temperature range 700 K to 1100 K. The temperature dependence of r predicted by Yang et al [37] is also in reasonably good agreement with the data. On evaluating the temperature dependence of r predicted by Eq.…”

Section: Temperature Dependence Of the Interfacial Free Energysupporting

confidence: 70%

“…[23]. Their inclusion in Figure 12, however, demonstrates that the values of r obtained from their data are not very far off from the overall trend, perhaps with the exception of the datum for t N = 40 h. The data in Figure 12(a) are compared with the results of several theoretical predictions in Figure 12(b), including those of Woodward et al [25] , Mao et al, [35] Mishin, [36] Yang et al [37] , Liu et al [38] and Kaptay. [17] Woodward et al…”

Section: Temperature Dependence Of the Interfacial Free Energymentioning

confidence: 90%

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Temperature Dependence of the γ/γ′ Interfacial Energy in Binary Ni–Al Alloys

Ardell

2021

Metall Mater Trans A

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Published data on the coarsening kinetics of γ′ (Ni3Al) precipitates in binary Ni–Al alloys aged at 12 temperatures ranging from 773 K to 1073 K are analyzed to provide a comprehensive evaluation of the temperature dependence of the γ/γ′ interfacial free energy, σ. The data are analyzed using equations of the trans-interface-diffusion-controlled (TIDC) theory of coarsening, with temporal exponent n = 2.4. The results show that σ decreases with increasing temperature, T. A linear empirical equation is fitted to the data on σvsT; it extrapolates to σ = 0 in the liquid region of the Ni–Al phase diagram, as it should do. A quantitative temperature-dependent transition radius, rtrans, is calculated; it depends on the product of the interface width and the ratio of the chemical diffusion coefficients in the γ phase and interface regions. Applying the TIDC coarsening equations to calculate σ is justified when the average radius, 〈r〉, satisfies the condition 〈r〉 < rtrans, which is valid for all the data used in the fit. The data on σvsT are compared with theoretical predictions. The results are discussed in the context of previous work, as well as with values of σ obtained through analyses using the equations of traditional LSW coarsening kinetics, n = 3.

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Section: Temperature Dependence Of the Interfacial Free Energysupporting

confidence: 84%

Section: Temperature Dependence Of the Interfacial Free Energysupporting

confidence: 70%

Section: Temperature Dependence Of the Interfacial Free Energymentioning

confidence: 90%

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Temperature Dependence of the γ/γ′ Interfacial Energy in Binary Ni–Al Alloys

Ardell

2021

Metall Mater Trans A

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“…Surface tension of solutions and surface adsorption of their components are key subjects of colloid chemistry − and nano-thermodynamics. − The combination of the two Butler equations offers a useful tool, allowing the estimation of both equilibrium surface composition and equilibrium surface tension of solution. − However, the Butler equations are used less frequently than they could/should be. One of the reasons is their awkward derivation, in which the partial surface tensions of the components are introduced without being properly defined.…”

Section: Introductionmentioning

confidence: 99%

Improved Derivation of the Butler Equations for Surface Tension of Solutions

Kaptay

2019

Langmuir

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The Butler equation was published in 1932 to describe the equilibrium surface composition and equilibrium surface tension of solutions. Unfortunately, it used the so-called “partial surface tension of a component”, which was not properly defined by Butler, leading to a reluctant acceptance of this equation. Although the present author defined the partial surface tension recently in this journal, it is considered an advantage to derive the same key equations of Butler without the need to employ the concept of partial surface tension. This derivation is offered in the present paper, starting from the two fundamental equations of Gibbs. No assumptions are made on the thickness and structure of the surface region, it is only supposed that the surface region has an average composition with a negligible concentration gradient. In this way, the Butler equations are obtained, which have more general validity compared to the original Butler equations derived by supposing a surface monolayer.

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“…In fact, this is also the ultimate goal in the field of materials science. To date, many approaches are available for theoretical alloy design, such as first-principles (FP) calculations [39] , molecular dynamics simulations [40] , computational thermodynamics (CT) [41][42][43] , computational kinetics [44][45][46][47][48][49][50][51] in the framework of CALculation of PHAse Diagram (CALPHAD) technique, phase-field simulations [52][53][54][55][56] , machine learning (ML) approach [57][58][59][60] , and other empirical/semi-empirical formulas [61][62][63] . Among them, CT in the framework of CALPHAD technique and data-driven ML method is the most advantageous approach for multicomponent alloy design.…”

Section: Introductionmentioning

confidence: 99%

Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

Wang¹,

Liu²,

Gao³

et al. 2021

JMI

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Casting aluminum alloys are commonly used in industries due to their excellent comprehensive performance. Alloying/microalloying and post-solidification heat treatments are the most common measures to tune the microstructure for enhancing their mechanical properties. However, it is very challenging to achieve accurate and efficient development of novel casting aluminum alloys using the traditional trial-and-error method. With the rapid development of computer technology, the computational thermodynamics (CT) in the framework of the CALculation of PHAse Diagram approach, the data-driven machine learning (ML) technique, and also their combinations have been proved to be effective approaches for the design of casting aluminum alloys. In this review, the state-of-the-art computational alloy design approaches driven by CT and ML techniques, as well as their combinations, were comprehensively summarized. The current status of the thermodynamic database for aluminum alloys, as the core for CT, was also briefly introduced. After that, a variety of successful case studies on the design of different casting aluminum alloys driven by CT, ML, and their combinations were demonstrated, including common applications, CT-driven design of Sc-additional Al-Si-Mg series casting alloys, and design of Srmodified A356 alloys driven by combing CT and ML. Finally, the conclusions of this review were drawn, and perspectives for boosting the computational design approach driven by combining CT and ML techniques were pointed out.

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OpenIEC: an open-source code for interfacial energy calculation in alloys

Cited by 16 publications

References 79 publications

Temperature Dependence of the γ/γ′ Interfacial Energy in Binary Ni–Al Alloys

Temperature Dependence of the γ/γ′ Interfacial Energy in Binary Ni–Al Alloys

Improved Derivation of the Butler Equations for Surface Tension of Solutions

Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

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