Chemical-element-distribution-mediated deformation partitioning and its control mechanical behavior in high-entropy alloys

Li, Jia; Xie, Bin; He, Quanfeng; Liu, Bin; Zeng, Xierong; Liaw, Peter K.; Fang, Qihong; Yang, Yong; Liu, Yong

doi:10.1016/j.jmst.2021.11.065

Cited by 9 publications

(3 citation statements)

References 41 publications

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“…High-entropy alloys (HEAs) are classified by configurational entropy of mixing ( ) 1 , in which the criteria are ≥ 1.36R and ≥ 1.50R for tetra and penta-metallic alloys 2 , respectively. This material has been employed in various applications due to its promising properties, especially catalytic 3 – 8 and mechanical properties 9 – 12 . Nowadays, discovering new formulae of HEA via experimental techniques requires a high cost of chemicals and characterization 13 , where the phase and atomic composition are challenges for HEA materials 14 .…”

Section: Introductionmentioning

confidence: 99%

“…Apart from such an algorithm, numerous techniques, i.e., the gradient boosting model, trained with 1,807 datasets, demonstrated high accuracy of 96.41% for predicting single-phase and non-single-phase refractory HEAs (RHEAs) 22 . Other methods also exhibit high accuracy prediction, e.g., a combined ML and CALPHAD technique, an artificial neural network technique (ANN) coupled with experimental data 9 , 10 , 12 , 23 – 28 . In addition to phase formation of HEAs, machine learning was recently employed to predict the mechanical properties of HEA bulk materials, including microhardness 10 , 27 , yield strength 12 , 23 , dislocation density 12 , elastic modulus 29 , Young’s modulus 30 , hardness 11 , 31 , and elastic constant 32 .…”

Section: Introductionmentioning

confidence: 99%

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High-throughput materials screening algorithm based on first-principles density functional theory and artificial neural network for high-entropy alloys

Rittiruam

Noppakhun

Setasuban

et al. 2022

Sci Rep

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This work introduced the high-throughput phase prediction of PtPd-based high-entropy alloys via the algorithm based on a combined Korringa-Kohn-Rostoker coherent potential approximation (KKR-CPA) and artificial neural network (ANN) technique. As the first step, the KKR-CPA was employed to generate 2,720 data of formation energy and lattice parameters in the framework of the first-principles density functional theory. Following the data generation, 15 features were selected and verified for all HEA systems in each phase (FCC and BCC) via ANN. The algorithm exhibited high accuracy for all four prediction models on 36,556 data from 9139 HEA systems with 137,085 features, verified by R2 closed to unity and the mean relative error (MRE) within 5%. From this dataset comprising 5002 and 4137 systems of FCC and BCC phases, it can be realized based on the highest tendency of HEA phase formation that (1) Sc, Co, Cu, Zn, Y, Ru, Cd, Os, Ir, Hg, Al, Si, P, As, and Tl favor FCC phase, (2) Hf, Ga, In, Sn, Pb, and Bi favor BCC phase, and (3) Ti, V, Cr, Mn, Fe, Ni, Zr, Nb, Mo, Tc, Rh, Ag, Ta, W, Re, Au, Ge, and Sb can be found in both FCC and BCC phases with comparable tendency, where all predictions are in good agreement with the data from the literature. Thus, the combination of KKR-CPA and ANN can reduce the computational cost for the screening of PtPd-based HEA and accurately predict the structure, i.e., FCC, BCC, etc.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-throughput materials screening algorithm based on first-principles density functional theory and artificial neural network for high-entropy alloys

Rittiruam

Noppakhun

Setasuban

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Moreover, local chemical orders exist predominantly in HEA matrices [19][20][21]. Such unique microstructures can trigger strong fluctuations in the local stacking-fault energy and change the dislocation slip mode in HEAs [19][20][21][22][23][24][25]. Resultantly, both lattice distortion and local chemical orders significantly affect the mechanical properties of HEAs.…”

Section: Introductionmentioning

confidence: 99%

Phase dissolution strengthens and ductilizes a high-entropy alloy

Wang

Tang

et al. 2022

Sci. China Mater.

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Annealing is widely used in the thermomechanical processing of metallic alloys. Usually, ductility enhances while strength decreases because defects annihilate with the increase in annealing temperature. Here, we report a simultaneous improvement in strength and ductility of a TiZrNbTa high-entropy alloy by promoting phase dissolution with increasing annealing temperature. When annealing is performed from 800 to 1250°C, the tensile yield strength increases by 40% to 1003 ± 16 MPa, and the elongation is nearly doubled to 16.79% ± 1.03%. The annealing-induced phase dissolution promotes lattice distortion, contributing to enhanced lattice friction stress and yield strength. The correspondingly decreased interface mismatch alleviates stress concentration, and the stimulated formation of local chemical orders encourages coplanar slip and dislocation multiplication. Both occurrences boost ductility. These results not only expand our understanding of the phase dissolution and annealing effect in metallic materials, but also provide an insight into designing strong and ductile alloys.

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Uniaxial deformation of nanowires in 16 refractory multi-principal element alloys

Mamun

et al. 2023

Journal of Alloys and Compounds

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Chemical-element-distribution-mediated deformation partitioning and its control mechanical behavior in high-entropy alloys

Cited by 9 publications

References 41 publications

High-throughput materials screening algorithm based on first-principles density functional theory and artificial neural network for high-entropy alloys

High-throughput materials screening algorithm based on first-principles density functional theory and artificial neural network for high-entropy alloys

Phase dissolution strengthens and ductilizes a high-entropy alloy

Uniaxial deformation of nanowires in 16 refractory multi-principal element alloys

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