First-principles study on the mechanical and thermodynamic properties of MoNbTaTiW

Bhandari, Uttam; Zhang, Congyan; Guo, Shengmin; Yang, Shizhong

doi:10.1007/s12613-020-2077-1

Cited by 23 publications

(8 citation statements)

References 41 publications

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“…The thermal expansion of MoNbTaTiW calculated in this work, both through density functional perturbation theory and from the rule of mixtures, are lower than what has previously been reported, for example, by previous modelling work by Bhandari et al [37]. Below 500 K, the rule of mixtures predicts a slightly lower thermal expansion coefficient compared to the DFPT, as shown in figure 4.…”

Section: Thermal Expansioncontrasting

confidence: 61%

“…DFT methods [37,38] were used with projector augmented wave style pseudopotentials [39] as implemented in the Vienna ab initio simulation package [39,40]. The exchange-correlation interactions were described by the generalised gradient approximation of the Perdew-Burke-Ernzerhof form [41].…”

Section: Methodsmentioning

confidence: 99%

“…Using the Thermo-calc software with the TTNI 8 database they found that MoNbTaTiW has a BCC structure at high temperatures, with an HCP phase appearing below 640 • C, although, as far as the authors are aware, this phase has not been reported experimentally. Bhandari et al [37] calculated the thermal expansion coefficient as a function of temperature of the equiatomic MoNbTaTiW alloy using density functional theory (DFT), on a 100-atom randomised supercell, and Debye theory, predicting a linear thermal expansion coefficient which rises steeply with temperature and plateaus at 550 K at 9 ×10 −6 K −1 [37]. In this work, we demonstrate that the temperature dependence of HEA thermal expansion coefficients can be predicted using computationally efficient special quasi-random structure (SQS) supercells and first-principles density functional perturbation theory.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the thermal expansion of body-centred cubic (BCC) high entropy alloys in the Mo–Nb–Ta–Ti–W system

et al. 2022

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In this study, the thermal expansion behaviour of equiatomic alloys in the Mo-Nb-Ta-Ti-W system is studied to provide a predictive method to assess the behaviour of this and other high entropy alloy systems. The simulations used are based on first principles density functional perturbation theory and the quasi-harmonic approximation. Calculations have been used to predict the stability and phonon properties of increasingly complex alloys in the Mo-Nb-Ta-Ti-W system and their thermal expansion coefficients have been predicted. These are benchmarked against rule of mixtures predictions and experimental observations where available. We have shown that atomic scale modelling techniques can be used to reliably predict thermal expansion of a range of BCC high entropy alloys and concentrated solid solutions of relevance to nuclear fusion and fission applications.

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Section: Thermal Expansioncontrasting

confidence: 61%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting the thermal expansion of body-centred cubic (BCC) high entropy alloys in the Mo–Nb–Ta–Ti–W system

et al. 2022

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“…Mishra et al calculated the bulk modulus and shear modulus for the R5 using supercell models and the DFT, which are reported as 193.90 GPa and 70.67 GPa, respectively [49]. Similarly, Bhandari et al calculated B and G using the Knuth-shuffle supercell model and the DFT, which are 199 GPa and 60 GPa, respectively [50]. This shows that the theoretical results for various models are pretty consistent.…”

Section: Mtp For Quinary Monbtatiwmentioning

confidence: 93%

Machine learning interatomic potential for high throughput screening and optimization of high-entropy alloys

Pandey¹,

Gigax²,

Pokharel³

2022

Preprint

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We have developed a machine learning-based interatomic potential (MLIP) for the quaternary MoNbTaW (R4) and quinary MoNbTaTiW (R5) high-entropy alloys (HEAs). MLIPs enabled accurate highthroughput calculations of elastic and mechanical properties of various non-equimolar R4 and R5 alloys, which are otherwise very timeconsuming calculations when performed using density functional theory (DFT). We demonstrate that the MLIP predicted properties compare well with the DFT results on various test cases and are consistent with the available experimental data. The MLIPs are also utilized for high-throughput optimization of non-equimolar R4 candidates by guided iterative tuning of R4 compositions to discover candidate materials with promising hardness-ductility combinations. We also used this approach to study the effect of Ti concentration on the elastic and mechanical properties of R4, by statistically averaging the properties of over 100 random structures. MLIP predicted hardness and bulk modulus of equimolar R4 and R5 HEAs are validated using experimentally measured Vicker's hardness and modulus. This approach opens a new avenue for employing MLIPs for HEA candidate optimization.

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“…Thus, application of RHEAs can be very promising candidates in the field of aerospace industry. Many studies of RHEAs such as TaNbHfZrTi, TiZrNbMoV, NbMoTaW, VNbMoTaW, Ti 2 ZrHf 0.5 MoNbx, and Al 0.5 CoCrCuFeNi have shown excellent yield strength both at room and high temperatures [5][6][7][8][9][10]. However, the poor ductility at room temperature of RHEAs limits their industrial application.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation

et al. 2021

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Hardness is an essential property in the design of refractory high entropy alloys (RHEAs). This study shows how a neural network (NN) model can be used to predict the hardness of a RHEA, for the first time. We predicted the hardness of several alloys, including the novel C0.1Cr3Mo11.9Nb20Re15Ta30W20 using the NN model. The hardness predicted from the NN model was consistent with the available experimental results. The NN model prediction of C0.1Cr3Mo11.9Nb20Re15Ta30W20 was verified by experimentally synthesizing and investigating its microstructure properties and hardness. This model provides an alternative route to determine the Vickers hardness of RHEAs.

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First-principles study on the mechanical and thermodynamic properties of MoNbTaTiW

Cited by 23 publications

References 41 publications

Predicting the thermal expansion of body-centred cubic (BCC) high entropy alloys in the Mo–Nb–Ta–Ti–W system

Predicting the thermal expansion of body-centred cubic (BCC) high entropy alloys in the Mo–Nb–Ta–Ti–W system

Machine learning interatomic potential for high throughput screening and optimization of high-entropy alloys

Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation

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