Generalized stacking fault energies and Peierls stresses in refractory body-centered cubic metals from machine learning-based interatomic potentials

Wang, Xiaowang; Xu, Shuozhi; Jian, Wu-Rong; Li, Xiangguo; Su, Yanqing; Beyerlein, Irene J.

doi:10.1016/j.commatsci.2021.110364

Cited by 41 publications

(25 citation statements)

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“…The strengthening prevails provided that the dislocation does not cross slip over the complex, which occurs when He:V = 6. The calculations also confirm that the screw to edge stress ratio reduces to 2–3, which is a substantial reduction from 10 to 20, the ratios without complexes …”

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confidence: 52%

Atomic-Scale Hidden Point-Defect Complexes Induce Ultrahigh-Irradiation Hardening in Tungsten

et al. 2021

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Tungsten displays high strength in extreme temperature and radiation environments and is considered a promising plasma facing material for fusion nuclear reactors. Unlike other metals, it experiences substantial irradiation hardening, which limits service life and presents safety concerns. The origin of ultrahigh-irradiation hardening in tungsten cannot be well-explained by conventional strengthening theories. Here, we demonstrate that irradiation leads to near 3-fold increases in strength, while the usual defects that are generated only contribute less than one-third of the hardening. An analysis of the distribution of tagged atom–helium ions reveals that more than 87% of vacancies and helium atoms are unaccounted for. A large fraction of helium–vacancy complexes are frozen in the lattice due to high vacancy migration energies. Through a combination of in situ nanomechanical tests and atomistic calculations, we provide evidence that irradiation hardening mainly originates from high densities of atomic-scale hidden point-defect complexes.

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confidence: 52%

Atomic-Scale Hidden Point-Defect Complexes Induce Ultrahigh-Irradiation Hardening in Tungsten

et al. 2021

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“…The Peierls stress was calculated to be about 2 GPa, consistent with the DFT prediction. Using a SNAP potential, Wang et al studied the GSFEs and Peierls stresses in refractory BCC metals Mo, Ta, Nb, and W [180]. Reasonable agreement with DFT was achieved for all the metals.…”

Section: Ong Et Al Investigated the Strengthening Mechanisms In Monbt...mentioning

confidence: 99%

Machine learning for high-entropy alloys: Progress, challenges and opportunities

Liu

Zhang

Pei

2023

Progress in Materials Science

101

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“…SNAP is used to calculate the generalized stacking fault energies (GSFEs) and Peierls stresses of screw and edge dislocations in four bcc refractory metals 108 . A universal strategy for determining crystal defects based on statistical outlier detection is proposed 106 .…”

Section: Applicationsmentioning

confidence: 99%

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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Generalized stacking fault energies and Peierls stresses in refractory body-centered cubic metals from machine learning-based interatomic potentials

Cited by 41 publications

References 100 publications

Atomic-Scale Hidden Point-Defect Complexes Induce Ultrahigh-Irradiation Hardening in Tungsten

Atomic-Scale Hidden Point-Defect Complexes Induce Ultrahigh-Irradiation Hardening in Tungsten

Machine learning for high-entropy alloys: Progress, challenges and opportunities

Machine‐learning‐based interatomic potentials for advanced manufacturing

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