Lattice distortion as an estimator of solid solution strengthening in high-entropy alloys

Roy, Abhinaba; Sreeramagiri, Praveen; Babuska, Tomas F.; Krick, Brandon A.; Ray, Pratik K.; Balasubramanian, Ganesh

doi:10.1016/j.matchar.2021.110877

Cited by 97 publications

(21 citation statements)

References 61 publications

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“…The geometrical parameter λ, which is a function of mixing entropy ( ) and the difference in atomic radii (δ) has a significant impact on Young’s modulus. The δ parameter has an impact on cohesive energy and Young’s modulus increases with increasing cohesive energy 56 , 57 . In our case, we have seen that a lower value of δ results in higher Young’s modulus as presented in Fig.…”

Section: Resultsmentioning

confidence: 99%

Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

Khakurel

Taufique

Roy

et al. 2021

Sci Rep

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We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young’s modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young’s modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.

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Section: Resultsmentioning

confidence: 99%

Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

Khakurel

Taufique

Roy

et al. 2021

Sci Rep

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“…Figure 3(d) presents the XRD analysis of the AlCoCrFeNi HEA and evinces that the alloy exhibits a BCC/B2 phase at 800 K. A closer scrutiny of the XRD reveals a slight peak broadening that can be attributed to the lattice distortion/strain in the alloy at higher cooling rates. [12] Figure 3(e) presents the RDF analysis of the alloy at various temperatures during cooling at 4.5 9 10 8 K/s. The volume of atoms is excluded in the RDF and thus a probability of zero or near zero is noted till 2 A ˚.…”

Section: Crystallographic Phase Evolutionmentioning

confidence: 99%

“…[1][2][3][4][5][6][7][8][9] Nonetheless, the contributions due to the random lattice site occupancies of the constituent elements, lattice strain, chemical mixing enthalpies, structural mismatch of competing phases, can promote a combination of solid-solution with intermetallic phases at equilibrium. [10][11][12][13] However, intermetallics usually form at a relatively lower cooling rate during the alloy synthesis or upon annealing at elevated temperatures for prolonged periods. [14,15] Among the widely examined HEA families, those containing Al and Cu have generated significant interest due to occurrence of metastable phases.…”

Section: Introductionmentioning

confidence: 99%

Effect of Cooling Rate on the Phase Formation of AlCoCrFeNi High-Entropy Alloy

Sreeramagiri

Roy

Balasubramanian

2021

J. Phase Equilib. Diffus.

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High-entropy alloys have received significant attention because of remarkable structural properties exhibited by certain alloy compositions. However, these properties are strongly correlated to the crystallographic phase transformations that are endured during the synthesis of these alloys. Using molecular dynamics simulations, we examine how the cooling rates exerted on the alloy melt during synthesis impact the crystallization (and glass formation) of equiatomic AlCoCrFeNi high-entropy alloy. An increased cooling rate contributes to severe undercooling of the alloy, reducing the crystallization temperatures and promotes phase transformations. We predict a critical cooling rate of 2.5 9 10 10 K/s beyond which the alloy tends to solidify into an amorphous phase. Our results reveal that higher cooling rates exert severe lattice distortion and significantly enhance the structural properties due to increase in dislocation density and deformation by twinning.

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“…To enhance mechanical properties like hardness and elastic modulus at elevated temperatures [2,[6][7][8], the principle was to add more elements and to maximize the configurational entropy to favor formation of simpler single-phase alloys [2,9]. But, in the past several years, research on metastable MPEAs has revealed many alloys violate this principle [10][11][12]. Hence, the goal shifted towards developing alloys that may not contain as many elements in equal proportions, but rather a complex mix in an optimized fashion to obtain the best properties [4,13], including in medium-entropy (3 or 4 elements) alloys.…”

Section: Introductionmentioning

confidence: 99%

Vacancy Formation Energies and Migration Barriers in Multi-Principal Element Alloys

et al. 2021

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Lattice distortion as an estimator of solid solution strengthening in high-entropy alloys

Cited by 97 publications

References 61 publications

Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

Effect of Cooling Rate on the Phase Formation of AlCoCrFeNi High-Entropy Alloy

Vacancy Formation Energies and Migration Barriers in Multi-Principal Element Alloys

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