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

Kostiuchenko, Tatiana; Körmann, Fritz; Neugebauer, Jörg; Shapeev, Alexander V.

doi:10.1038/s41524-019-0195-y

Cited by 150 publications

(88 citation statements)

References 34 publications

(82 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For theoretical studies, relatively large composite molecular models have to be developed to address this aspect, which are extensively computationally demanding to be studied by the DFT-based simulations. To address the stability under the water, we do believe that classical molecular dynamics simulations by using the machine learning potentials [54][55][56] may show a great prospect.…”

Section: Resultsmentioning

confidence: 99%

Two-Dimensional SiP, SiAs, GeP and GeAs as Promising Candidates for Photocatalytic Applications

et al. 2019

View full text Add to dashboard Cite

Group IV–V-type layered materials, such as SiP, SiAs, GeP and GeAs, are among the most attractive two-dimensional (2D) materials that exhibit anisotropic mechanical, optical and transport properties. In this short communication, we conducted density functional theory simulations to explore the prospect of SiP, SiAs, GeP and GeAs nanosheets for the water-splitting application. The semiconducting gaps of stress-free SiP, SiAs, GeP and GeAs monolayers were estimated to be 2.59, 2.34, 2.30 and 2.07 eV, respectively, which are within the desirable ranges for the water splitting. Moreover, all the considered nanomaterials were found to yield optical absorption in the visible spectrum, which is a critical feature for the employment in the solar water splitting systems. Our results furthermore confirm that the valence and conduction band edge positions in SiP, SiAs, GeP and GeAs monolayers also satisfy the requirements for the water splitting. Our results highlight the promising photocatalytic characteristics of SiP, SiAs, GeP and GeAs nanosheets for the application in solar water splitting and design of advanced hydrogen fuel cells.

show abstract

Section: Resultsmentioning

confidence: 99%

Two-Dimensional SiP, SiAs, GeP and GeAs as Promising Candidates for Photocatalytic Applications

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Benefiting from advanced learning algorithms and large databases using high-throughput computations, machine learning has been widely applied to materials research and discovery [32,33,34]. Some examples of successful applications include discovering complex materials behavior [35,36], accurate prediction of phase transitions and prediction [37,38,39], accelerated material design and prediction of material properties [40,41,42], modeling of various physical quantities, for instance, interatomic potentials [43,44,45] and atomic forces [44,46]. Compared to successful applications in other fields, few studies have been conducted in the context of machine learning for the modeling of thermodynamics of HEAs.…”

mentioning

confidence: 99%

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

Zhang

Liu

et al. 2020

Materials & Design

View full text Add to dashboard Cite

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...

show abstract

“…Besides clusters, NN potentials were used for multicomponent alloy surfaces and the predicted mean surface compositions for AuPd alloys showed good agreement with reported experimental results . Other alloys, such as NbMoTaW, were investigated by ML potentials in combination with Monte Carlo simulations . For oxides, Jacobsen et al investigated the surface reconstruction of SnO 2 (110)‐(4 × 1) based on ML potentials.…”

Section: Achievements Of ML In Energy Storage and Conversion Materialsmentioning

confidence: 69%

Machine learning: Accelerating materials development for energy storage and conversion

Chen

Zhou

2020

InfoMat

243

171

View full text Add to dashboard Cite

With the development of modern society, the requirement for energy has become increasingly important on a global scale. Therefore, the exploration of novel materials for renewable energy technologies is urgently needed. Traditional methods are difficult to meet the requirements for materials science due to long experimental period and high cost. Nowadays, machine learning (ML) is rising as a new research paradigm to revolutionize materials discovery.In this review, we briefly introduce the basic procedure of ML and common algorithms in materials science, and particularly focus on latest progress in applying ML to property prediction and materials development for energyrelated fields, including catalysis, batteries, solar cells, and gas capture. Moreover, contributions of ML to experiments are involved as well. We highly expect that this review could lead the way forward in the future development of ML in materials science. K E Y W O R D Sbig data, energy storage and conversion, machine learning, property prediction

show abstract

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

Cited by 150 publications

References 34 publications

Two-Dimensional SiP, SiAs, GeP and GeAs as Promising Candidates for Photocatalytic Applications

Two-Dimensional SiP, SiAs, GeP and GeAs as Promising Candidates for Photocatalytic Applications

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

Machine learning: Accelerating materials development for energy storage and conversion

Contact Info

Product

Resources

About