Revisit the VEC criterion in high entropy alloys (HEAs) with high-throughput ab initio calculations: A case study with Al-Co-Cr-Fe-Ni system

Yang, Songge; Liu, Guangchen; Zhong, Yu

doi:10.1016/j.jallcom.2022.165477

Cited by 28 publications

(10 citation statements)

References 48 publications

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“…As HEAs have been increasingly studied over the past decade, certain empirical rules have been established that correlate well with the observed properties [110] . In the past, it has been suggested that these empirical rules can provide a guideline surrounding the design of HEAs with desirable properties, such as the formation of single-phase solid solutions [111] . However, certain empirical rules, such as the VEC threshold for the stability of FCC and BCC solid solutions, have failed to maintain predictive accuracy over the , copyright 2018, Elsevier.…”

Section: First-principles Calculationsmentioning

confidence: 99%

“…The use of combining MD simulations with ML techniques was also explored by Zhang et al to explore the non-equiatomic compositions within the Fe-Co-Cr-Ni-Mn alloy system [124] . In this case, the deformation of 100 compositions with a single-crystal structure was simulated in three different crystallographic directions, [100], [110], and [111]. The simulated stress-strain responses of these compositions are shown in Figure 8A.…”

Section: Molecular Dynamicsmentioning

confidence: 99%

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A review on high-throughput development of high-entropy alloys by combinatorial methods

Mooraj¹,

Chen²

2023

J Mater Inf

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High-entropy alloys (HEAs) are an emerging class of alloys with multi-principal elements that greatly expands the compositional space for advanced alloy design. Besides chemistry, processing history can also affect the phase and microstructure formation in HEAs. The number of possible alloy compositions and processing paths gives rise to enormous material design space, which makes it challenging to explore by traditional trial-and-error approaches. This review highlights the progress in combinatorial high-throughput studies towards rapid prediction, manufacturing, and characterization of promising HEA compositions. This review begins with an introduction to HEAs and their unique properties. Then, this review describes high-throughput computational methods such as machine learning that can predict desired alloy compositions from hundreds or even thousands of candidates. The next section of this review presents advances in combinatorial synthesis of material libraries by additive manufacturing for efficient development of high-performance HEAs at bulk scale. The final section of this review discusses the high-throughput characterization techniques that are used to accelerate the material property measurements for systematic understanding of the composition-processing-structure-property relationships in combinatorial HEA libraries.

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Section: First-principles Calculationsmentioning

confidence: 99%

Section: Molecular Dynamicsmentioning

confidence: 99%

A review on high-throughput development of high-entropy alloys by combinatorial methods

Mooraj¹,

Chen²

2023

J Mater Inf

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“…This contradiction is not solved even if the revisited VEC rule is used which suggests that bcc structure is stable in the VEC range between 5 and 6.87. [ 54 ] Most probably, the phases formed during sputtering are not in thermodynamic equilibrium; therefore, they do not follow the traditional structural predictions. In the future, it is worth studying the influence of the processing way on the phase composition and the microstructure of MPEAs beside the effect of the chemistry, and the phase prediction methods must be improved accordingly.…”

Section: Characterization Of Combinatorial Mpeasmentioning

confidence: 99%

Combinatorial Design of Novel Multiprincipal Element Alloys Using Experimental Techniques

Gubicza

2023

Adv Eng Mater

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Multiprincipal element alloys (MPEAs) including high‐entropy alloys are in the focus of materials science since many MPEA compositions exhibit outstanding mechanical and physical properties. These alloys contain 3–6 constituents with similar fractions; therefore, MPEAs correspond to the unexplored middle part of the phase diagrams. The phase composition and the performance of MPEAs are strongly influenced by their chemistry. Thus, it is very important to reveal the correlation between the composition, the structure, and the properties of MPEAs. On the other hand, this task is challenging due to the vast composition space of these alloys. The processing and characterization of combinatorial specimens can yield a fast mapping of compositional libraries of MPEAs. Herein, the experimental techniques developed for manufacturing and investigation of these alloys are overviewed and the results are critically discussed. Finally, further development directions in the field of combinatorial MPEAs are recommended in order to improve the study of the different alloy compositions.

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“…The probable phase to be formed can be predicted using the VEC rule. However, an argument to the contrary was made in a recent study by Yang et al [26], who proposed a new approach known as the effectiveness of the VEC rule (EVEC). However, this approach cannot predict the stability of dual-phase regions such as the FCC + BCC phases.…”

Section: Hea Design and Modelingmentioning

confidence: 99%

Hot deformation behavior and constitutive modeling of a cost-effective Al8Cr12Mn25Ni20Fe35 high-entropy alloy

Abdel-Ghany

Jaskari

Hamada

et al. 2022

Journal of Alloys and Compounds

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Revisit the VEC criterion in high entropy alloys (HEAs) with high-throughput ab initio calculations: A case study with Al-Co-Cr-Fe-Ni system

Cited by 28 publications

References 48 publications

A review on high-throughput development of high-entropy alloys by combinatorial methods

A review on high-throughput development of high-entropy alloys by combinatorial methods

Combinatorial Design of Novel Multiprincipal Element Alloys Using Experimental Techniques

Hot deformation behavior and constitutive modeling of a cost-effective Al8Cr12Mn25Ni20Fe35 high-entropy alloy

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