Sublattice formation in CoCrFeNi high-entropy alloy

Meshkov, E.A.; Novoselov, I.I.; Shapeev, Alexander V.; Yanilkin, A. V.

doi:10.1016/j.intermet.2019.106542

Cited by 35 publications

(18 citation statements)

References 55 publications

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Section: Applicationsmentioning

confidence: 99%

“…The comparison results are obtained from DFT, LRP, and an EAM 80 potential and magnetic cluster expansion (MCE) 81. Reproduced with permission 79. Copyright 2019, Elsevier F I G U R E 9 A 900-atom amorphous Ge 2 Sb 2 Te 5 model is generated by classical molecular dynamics simulations with a Gaussian approximation potential, and then the electronic structure is calculated.…”

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confidence: 99%

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Machine‐learning‐based interatomic potentials for advanced manufacturing

Wan

et al. 2021

Int Journal of Mech Sys Dyn

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This paper summarizes the progress of machine-learning-based interatomic potentials and their applications in advanced manufacturing. Interatomic potential is essential for classical molecular dynamics. The advancements made in machine learning (ML) have enabled the development of fast interatomic potential with ab initio accuracy. The accelerated atomic simulation can greatly transform the design principle of manufacturing technology. The most widely used supervised and unsupervised ML methods are summarized and compared. Then, the emerging interatomic models based on ML are discussed: Gaussian approximation potential, spectral neighbor analysis potential, deep potential molecular dynamics, SCHNET, hierarchically interacting particle neural network, and fast learning of atomistic rare events.

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Section: Applicationsmentioning

confidence: 99%

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confidence: 99%

Machine‐learning‐based interatomic potentials for advanced manufacturing

Wan

et al. 2021

Int Journal of Mech Sys Dyn

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“…A large number of IAP workflows rely on neural networks in order to approximate energies and forces from atomic positions [331], [332], [333], [334], [335], [336], [337], [338], [339], [340], [341], [342], [343], [344], [345], [346], [347], [348], [349], [350], [351], [352], [353], [354], [316], [355], [356], [357], [358], [359], [360], [361], [362], [363], [364], [365], [366], [367], [368].…”

Section: Neural Network Potentialsmentioning

confidence: 99%

A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry

Fiedler¹,

Shah²,

Bußmann³

et al. 2021

Preprint

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With the growth of computational resources, the scope of electronic structure simulations has increased greatly. Artificial intelligence and robust data analysis hold the promise to accelerate large-scale simulations and their analysis to hitherto unattainable scales. Machine learning is a rapidly growing field for the processing of such complex datasets. It has recently gained traction in the domain of electronic structure simulations, where density functional theory takes the prominent role of the most widely used electronic structure method. Thus, DFT calculations represent one of the largest loads on academic high-performance computing systems across the world. Accelerating these with machine learning can reduce the resources required and enables simulations of larger systems. Hence, the combination of density functional theory and machine learning has the potential to rapidly advance electronic structure applications such as in-silico materials discovery and the search for new chemical reaction pathways. We provide the theoretical background of both density functional theory and machine learning on a generally accessible level. This serves as the basis of our comprehensive review including research articles up to December 2020 in chemistry and materials science that employ machine-learning techniques. In our analysis, we categorize the body of research into main threads and extract impactful results. We conclude our review with an outlook on exciting research directions in terms of a citation analysis.

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“…Such data sets are very small and are unlikely to accurately predict a wide range of properties for the vast configuration space of these alloys. Only recently, MLIPs have been used for efficient optimization of HEA properties [23][24][25]. In this work, we demonstrate the application of MLIPs for high-throughput screening of novel HEAs that exhibit promising hardness-ductility combinations, which addresses the hardness-ductility trade-off in HEAs [19,26].…”

Section: Introductionmentioning

confidence: 97%

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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Sublattice formation in CoCrFeNi high-entropy alloy

Cited by 35 publications

References 55 publications

Machine‐learning‐based interatomic potentials for advanced manufacturing

Machine‐learning‐based interatomic potentials for advanced manufacturing

A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry

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

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