Ab initio phase stabilities and mechanical properties of multicomponent alloys: A comprehensive review for high entropy alloys and compositionally complex alloys

Ikeda, Yûji; Grabowski, Blazej; Körmann, Fritz

doi:10.1016/j.matchar.2018.06.019

Cited by 289 publications

(164 citation statements)

References 220 publications

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Section: Introductionmentioning

confidence: 99%

“…Typically, the material properties are inherently linked to the actual state of chemical ordering, much efforts have been therefore devoted to analyze the degree of chemical ordering and to identify the order-disorder phase transitions [8,7,9,10]. Due to expensive time costs in experimental research, computational simulations, typically first-principles calculations are playing an increasingly central role in the investigation of various properties of HEAs [11,12,13].First-principles density functional theory (DFT) methods have established as a powerful and reliable tool in computational material science and have enabled critical advancements in materials properties and performance discovery [14,15]. With the increasing numerical efficiency and growing computing power (parallel and GPU computing), it is still difficult to address the challenge of DFT calculations in relatively large supercells (thousands of atoms) and intensive sampling (huge number of configurations) [16].…”

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confidence: 99%

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Robust data-driven approach for predicting the configurational energy of high entropy alloys

Zhang

Liu

et al. 2020

Materials & Design

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High entropy alloys (HEAs) have been increasingly attractive as promising next-generation materials due to their various excellent properties. It's necessary to essentially characterize the degree of chemical ordering and identify order-disorder transitions through efficient simulation and modeling of thermodynamics. In this study, a robust data-driven framework based on Bayesian approaches is proposed and demonstrated on the accurate and efficient prediction of configurational energy of high entropy alloys. The proposed effective pair interaction (EPI) model with ensemble sampling is used to map the configuration and its corresponding energy. Given limited data calculated by first-principles calculations, Bayesian regularized regression not only offers an accurate and stable prediction but also effectively quantifies the uncertainties associated with EPI parameters. Compared with the arbitrary determination of model complexity, we further conduct a physical feature selection to identify the truncation of coordination shells in EPI model using Bayesian information criterion. The results achieve efficient and robust performance in predicting the configurational energy, particularly given small data. The developed methodology is applied to study a series of refractory HEAs, i.e. NbMoTaW, NbMoTaWV and NbMoTaWTi where it is demonstrated how dataset size affects the confidence we can place in statistical estimates of configurational energy when data are sparse. IntroductionAs one of the typical multicomponent alloys, high entropy alloys (HEAs) consisting of four or more principal elements have been widely studied due to their exceptional mechanical properties [1,2,3,4]. The increased number of elements expand the possible combinations and potential candidates for discovering next-generation materials with enhanced properties [5,6,7]. Typically, the material properties are inherently linked to the actual state of chemical ordering, much efforts have been therefore devoted to analyze the degree of chemical ordering and to identify the order-disorder phase transitions [8,7,9,10]. Due to expensive time costs in experimental research, computational simulations, typically first-principles calculations are playing an increasingly central role in the investigation of various properties of HEAs [11,12,13].First-principles density functional theory (DFT) methods have established as a powerful and reliable tool in computational material science and have enabled critical advancements in materials properties and performance discovery [14,15]. With the increasing numerical efficiency and growing computing power (parallel and GPU computing), it is still difficult to address the challenge of DFT calculations in relatively large supercells (thousands of atoms) and intensive sampling (huge number of configurations) [16]. To characterize the orderdisorder phase transition, a straightforward way is to combine the DFT method with Monte Carlo simulations. However, this "brute-force" method is so computationally intensive that it is often i...

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Robust data-driven approach for predicting the configurational energy of high entropy alloys

Zhang

Liu

et al. 2020

Materials & Design

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“…1,2 As experimental approaches alone are too time-consuming to cover the vast variety of possible alloy combinations, parameter-free ab initio simulations, typically realized by density functional theory (DFT), have gained rapidly increasing attention as a complementary tool to study various properties of HEAs (see, e.g., ref. 5…”

Section: Introductionmentioning

confidence: 99%

Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials

et al. 2019

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Recently, high-entropy alloys (HEAs) have attracted wide attention due to their extraordinary materials properties. A main challenge in identifying new HEAs is the lack of efficient approaches for exploring their huge compositional space. Ab initio calculations have emerged as a powerful approach that complements experiment. However, for multicomponent alloys existing approaches suffer from the chemical complexity involved. In this work we propose a method for studying HEAs computationally. Our approach is based on the application of machine-learning potentials based on ab initio data in combination with Monte Carlo simulations. The high efficiency and performance of the approach are demonstrated on the prototype bcc NbMoTaW HEA. The approach is employed to study phase stability, phase transitions, and chemical short-range order. The importance of including local relaxation effects is revealed: they significantly stabilize single-phase formation of bcc NbMoTaW down to room temperature. Finally, a so-far unknown mechanism that drives chemical order due to atomic relaxation at ambient temperatures is discovered.npj Computational Materials (2019) 5:55 ; https://doi.

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“…However, as with steels, the complex chemistry of HEAs leads to almost unlimited combinations that make a systematic determination of this property difficult. Nevertheless, to accelerate the development of these alloys, several research groups have made use of ab initio calculations, e.g., [12]. The advantage of this method is that it is possible to calculate several alloy compositions with less effort than the one required for an experimental determination of the same alloys.…”

mentioning

confidence: 99%

“…So far, the systems CoCrFeMnNi [9,10,13,14], AlCoCrCuFeNi, and AlCoCrFeNi [15] have been investigated regarding their SFE. A comprehensive list can be found in Reference [12]. First-principle methods have even allowed the constructions of property maps, where the SFE can be read as a function of the chemical composition [14], which can be used to predict the expected physical acting mechanisms.…”

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confidence: 99%

From High-Manganese Steels to Advanced High-Entropy Alloys

Haase

Barrales‐Mora

2019

Metals

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Arguably, steels are the most important structural material, even to this day. Numerous design concepts have been developed to create and/or tailor new steels suited to the most varied applications. High-manganese steels (HMnS) stand out for their excellent mechanical properties and their capacity to make use of a variety of physical mechanisms to tailor their microstructure, and thus their properties. With this in mind, in this contribution, we explore the possibility of extending the alloy design concepts that haven been used successfully in HMnS to the recently introduced high-entropy alloys (HEA). To this aim, one HMnS steel and the classical HEA Cantor alloy were subjected to cold rolling and heat treatment. The evolution of the microstructure and texture during the processing of the alloys and the resulting properties were characterized and studied. Based on these results, the physical mechanisms active in the investigated HMnS and HEA were identified and discussed. The results evidenced a substantial transferability of the design concepts and more importantly, they hint at a larger potential for microstructure and property tailoring in the HEA. several core effects (high-entropy effect, sever lattice distortion effect, sluggish diffusion effect, cocktail effect) of HEAs have been proposed [5], a clear design strategy is still missing.The underlying physical mechanisms active in HMnS have already been studied for several decades [1,2,6], and therefore are comparatively well understood. One of the most important parameters when designing HMnS is the tailoring of their stacking fault energy (SFE). This property controls the dissociation distance of partial dislocations and determines whether TRIP and/or TWIP is activated/suppressed during plastic deformation. The role of the SFE during plastic deformation and subsequent heat treatment in metals has been studied for decades, and its effect is relatively well-known for face-centered cubic (fcc) metals [7]. In fcc HEAs, the mechanisms of microstructure formation have been found to occur in a similar way to the same class of alloys with comparable SFE [8]. For instance, the Cantor alloy (CoCrFeMnNi) with an estimated SFE between 18.3-27.3 mJ/m 2 [9,10] has been shown to develop the TWIP effect, depending on the processing conditions [11]. Evidently, the determination of the SFE in HEAs is fundamental, because this property indicates the possible acting mechanisms for microstructure development. However, as with steels, the complex chemistry of HEAs leads to almost unlimited combinations that make a systematic determination of this property difficult. Nevertheless, to accelerate the development of these alloys, several research groups have made use of ab initio calculations, e.g., [12]. The advantage of this method is that it is possible to calculate several alloy compositions with less effort than the one required for an experimental determination of the same alloys. So far, the systems CoCrFeMnNi [9,10,13,14], AlCoCrCuFeNi, and AlCoCrFeNi [15] have been investig...

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Ab initio phase stabilities and mechanical properties of multicomponent alloys: A comprehensive review for high entropy alloys and compositionally complex alloys

Cited by 289 publications

References 220 publications

Robust data-driven approach for predicting the configurational energy of high entropy alloys

Robust data-driven approach for predicting the configurational energy of high entropy alloys

Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials

From High-Manganese Steels to Advanced High-Entropy Alloys

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