Atomistic calculations of the generalized stacking fault energies in two refractory multi-principal element alloys

Xu, Shuozhi; Hwang, Emily; Jian, Wu-Rong; Su, Yanqing; Beyerlein, Irene J.

doi:10.1016/j.intermet.2020.106844

Cited by 49 publications

(14 citation statements)

References 68 publications

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“…This will severely increase the computational cost if the target is to investigate γ usf for a large amount of alloying compositions, for example, 106 compositions in the present work. On the other hand, due to the mirror symmetry of the (1 10) plane, γ usf of the (1 10) [111] slip in a bcc lattice should occur at a shift distance of | 1 4 [111]| although deviations may be induced by the local chemical variations and lattice distortions [62]. In the present work, for the sake of simplification, we used the GSF energies that correspond to the | 1 4 [111]| shifts on different fault planes in the supercell to derive the averaged γ usf for a given alloy composition.…”

Section: Resultsmentioning

confidence: 99%

Screening of generalized stacking fault energies, surface energies and intrinsic ductile potency of refractory multicomponent alloys

Sundar

Ogata

et al. 2020

Preprint

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Body-centered cubic (bcc) refractory multicomponent alloys are of great interest due to their remarkable strength at high temperatures. Meanwhile, further optimizing the chemical compositions of these alloys to achieve a combination of high strength and room-temperature ductility remains challenging, which would require systematic predictions of the correlated alloy properties across a vast compositional space. In the present work, we performed first-principles calculations with the special quasi-random structure (SQS) method to predict the unstable stacking fault energy (γ usf ) of the (1 10) [111] slip system and the (1 10)-plane surface energy (γ surf ) for 106 individual binary, ternary and quaternary bcc solid-solution alloys with constituent elements among Ti, Zr, Hf, V, Nb, Ta, Mo, W, Re and Ru. Moreover, with the first-principles data and a set of physics-informed descriptors, we developed surrogate models based on statistical regression to accurately and efficiently predict γ usf and γ surf for refractory multicomponent alloys in the 10-element compositional space. Building upon binary and ternary data, the surrogate models show outstanding predictive ability in the high-order multicomponent systems. The ratio between γ surf and γ usf is a parameter to reflect the potency of intrinsic ductility of an alloy based on the Rice model of crack-tip deformation. Therefore, using the surrogate models, we performed a systematic screening of γ usf , γ surf and their ratio over 112,378 alloy compositions to search for alloy candidates that may have enhanced strength-ductile synergies. Search results were also confirmed by additional first-principles calculations.

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Section: Resultsmentioning

confidence: 99%

Screening of generalized stacking fault energies, surface energies and intrinsic ductile potency of refractory multicomponent alloys

Sundar

Ogata

et al. 2020

Preprint

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“…The intrinsic γ sf e of alloys can vary over a large range of 150mJ/m 2 [40]. Earlier reports did not consider the chemistry of the shearing interface which influences the γ usf e of the overall alloy [12,20,[41][42][43][44][45][46][47][48][49].…”

Section: Unstable Stacking-fault Energy (γ Usf E )mentioning

confidence: 99%

“…Special quasirandom structures (SQS) have been extensively used to predict the SFE of alloys due to their simplicity in capturing the inherent chemical disorder present in the alloys [67]. There have been numerous reports on SFE of concentrated alloys calculated using SQS, albeit without any consideration to the shearing interface chemistry [12,20,[41][42][43][44][45][46][47][48][49]. There has been significant deviations in the literature about the reported γ usf e values even for the pure metals [9,16].…”

Section: Shearing Interface Chemistrymentioning

confidence: 99%

Designing a thermodynamically stable and intrinsically ductile refractory alloy

Shaikh¹,

Murty²,

Yadav³

2022

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The deformation behavior of BCC alloys is sensitive to temperature, strain rate, and alloying elements which alter their unstable stacking fault energy (USFE). Effect of substitutional alloying elements in significant fractions have not been explored in detail for refractory BCC alloys. Substitutional elements may decrease USFE, which can improve their deformability. Special quasirandom structure (SQS) supercells are widely used to calculate the USFE of alloys. Such SQS attempt to mimic the random occupancy of each lattice site by the constituent elements. This approach does not guarantee the same stoichiometry in the shearing interface as in the alloy formula, which results in considerable variation in calculated USFE depending on the type and number of atoms in the two planes being sheared. Here we propose a modified SQS methodology with correct shearing interface chemistry to reliably calculate the USFE of concentrated alloys. In present work, enthalpy and ductility parameter is calculated using First-principles Density Functional Theory simulation. USFE of (110)<111>slip system is calculated for Group IV,V, VI elements & their 30 equiatomic binary alloys using a modified SQS methodology which reduces the number of DFT jobs required from the current 81 to 9. The deformability of refractory BCC alloys is correlated with their thermodynamic stability.

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“…We then need to repeat the calculations for different random configurations in order to obtain the average SFE of the random alloy. The "magic" minimum number, i.e., the number mostly reported in other works [36,11], of required configurations is 28 when the atom types are distributed using the SQS method (cf., e.g., [36,11]). Additionally assuming that we are interested in the average SFE at 5-10 different compositions of the random alloy, we already require more than 10 000 DFT calculations corresponding to two months of absolute computing time.…”

Section: A Few Remarks On Computing the Sfe With Dftmentioning

confidence: 99%

“…Several types of common supercell boundary conditions are discussed in the following (see, e.g., [6,36,11]. One possibility is to apply periodic boundary conditions to the supercells containing {r i } bulk and {r i } sf .…”

Section: Introductionmentioning

confidence: 99%

Machine-learning potentials enable predictive $\textit{and}$ tractable high-throughput screening of random alloys

Hodapp,

Shapeev

2021

Preprint

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We present an automated procedure for computing stacking fault energies in random alloys from large-scale simulations using moment tensor potentials (MTPs) with the accuracy of density functional theory (DFT). To that end, we develop an algorithm for training MTPs on random alloys. In the first step, our algorithm constructs a set of ∼ 10 000 or more training candidate configurations with 50-100 atoms that are representative for the atomic neighborhoods occurring in the large-scale simulation. In the second step, we use active learning to reduce this set to ∼ 100 most distinct configurations-for which DFT energies and forces are computed and on which the potential is ultimately trained. We validate our algorithm for the MoNbTa medium-entropy alloy by showing that the MTP reproduces the DFT 1 4 [111] unstable stacking fault energy over the entire compositional space up to a few percent.Contrary to state-of-the-art methods, e.g., the coherent potential approximation (CPA) or special quasi-random structures (SQSs), our algorithm naturally accounts for relaxation, is not limited by DFT cell sizes, and opens opportunities to efficiently investigate follow-up problems, such as chemical ordering. In a broader sense, our algorithm can be easily modified to compute related properties of random alloys, for instance, misfit volumes, or grain boundary energies. Moreover, it forms the basis for an efficient construction of MTPs to be used in large-scale simulations of multicomponent systems.

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Atomistic calculations of the generalized stacking fault energies in two refractory multi-principal element alloys

Cited by 49 publications

References 68 publications

Screening of generalized stacking fault energies, surface energies and intrinsic ductile potency of refractory multicomponent alloys

Screening of generalized stacking fault energies, surface energies and intrinsic ductile potency of refractory multicomponent alloys

Designing a thermodynamically stable and intrinsically ductile refractory alloy

Machine-learning potentials enable predictive $\textit{and}$ tractable high-throughput screening of random alloys

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