Abnormally high thermal conductivity in fivefold twinned diamond nanowires

Liang, Ting; Xu, Ke; Han, Meng; Yao, Yimin; Zhang, Zhisen; Zeng, Xiaoliang; Xu, Jianbin; Wu, Jianyang

doi:10.1016/j.mtphys.2022.100705

Cited by 14 publications

(31 citation statements)

References 57 publications

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“…This trend is consistent with previous studies of 5FT diamond NWs. 19 Previous studies uncovered that this “flipped” behavior in κ of extremely thin NWs can be attributed to the hydrodynamic phonon flow due to the normal (N) three-phonon scattering process in the low-frequency region. 18–20…”

Section: Resultsmentioning

confidence: 99%

“…To gain a deeper physical understanding of the anomalous heat transport behavior in 5FT-NWs, three-phonon scattering rates are calculated. As is known, when heat is transported in extremely thin NWs composed of Si, 18,20 GaP 68 and C, 19 the dominant process for heat transport is the N-process, resulting in a hydrodynamic phonon “fluid” flow. This N-process leads to an increase in κ , while the weaker U-process also exists.…”

Section: Resultsmentioning

confidence: 99%

“…Very recently, it was revealed that as a result of phonon hydrodynamic flow formed in the Si-NWs, extremely thin NWs show abnormally high κ . 17–20 Compared with layered materials, 21–24 however, κ of Si-NWs remains relatively high. A further reduction in the κ of Si-NWs is necessary to achieve enhanced performance of thermoelectric conversion.…”

Section: Introductionmentioning

confidence: 99%

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Thermal conductivity of fivefold twinned silicon-germanium heteronanowires

Zhou,

Zeng,

Song

et al. 2023

Phys. Chem. Chem. Phys.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Thermal conductivity of fivefold twinned silicon-germanium heteronanowires

Zhou,

Zeng,

Song

et al. 2023

Phys. Chem. Chem. Phys.

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“…It has also been used to study the particular thermal transport properties of specific materials, in-cluding various two-dimensional (2D) materials, 64,[89][90][91][92][93][94][95][96][97][98][99][100][101][102][103] vdW structures based on 2D materials, [104][105][106][107][108][109][110] and quasi-one-dimensional materials. [111][112][113] There are applications focused on revealing unique phonon transport mechanisms. [114][115][116][117][118][119][120] The high efficiency of gpumd also enabled high-throughput thermal transport simulations that were used as training/testing data for machine learning models of interfacial thermal transport.…”

Section: Overview Of the Gpumd Packagementioning

confidence: 99%

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

Fan

Wang

Ying

et al. 2022

The Journal of Chemical Physics

112

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We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in [Fan et al., Phys. Rev. B 104, 104309 (2021)] and their implementation in the open-source package GPUMD.We increase the accuracy of NEP models both by improving the radial functions in the atomic-environment descriptor using a linear combination of Chebyshev basis functions and by extending the angular descriptor with some four-body and five-body contributions as in the atomic cluster expansion approach.We also detail our efficient implementation of the NEP approach in graphics processing units as well as our workflow for the construction of NEP models, and we demonstrate their application in large-scale atomistic simulations.By comparing to state-of-the-art MLPs, we show that the NEP approach not only achieves above-average accuracy but also is far more computationally efficient.These results demonstrate that the GPUMD package is a promising tool for solving challenging problems requiring highly accurate, large-scale atomistic simulations.To enable the construction of MLPs using a minimal training set, we propose an active-learning scheme based on the latent space of a pre-trained NEP model.Finally, we introduce three separate Python packages, GPYUMD, CALORINE, and PYNEP, which enable the integration of GPUMD into Python workflows.

show abstract

Section: Overview Of the Gpumd Packagementioning

confidence: 99%

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

Fan,

Wang,

Ying

et al. 2022

Preprint

View full text Add to dashboard Cite

We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in [Fan et al., Phys. Rev. B 104, 104309 (2021)] and their implementation in the open-source package gpumd. We increase the accuracy of NEP models both by improving the radial functions in the atomic-environment descriptor using a linear combination of Chebyshev basis functions and by extending the angular descriptor with some four-body and five-body contributions as in the atomic cluster expansion approach. We also detail our efficient implementation of the NEP approach in graphics processing units as well as our workflow for the construction of NEP models, and we demonstrate their application in large-scale atomistic simulations. By comparing to state-of-the-art MLPs, we show that the NEP approach not only achieves above-average accuracy but also is far more computationally efficient. These results demonstrate that the gpumd package is a promising tool for solving challenging problems requiring highly accurate, large-scale atomistic simulations. To enable the construction of MLPs using a minimal training set, we propose an active-learning scheme based on the latent space of a pre-trained NEP model. Finally, we introduce two separate Python packages, gpyumd and calorine, which enable the integration of gpumd into Python workflows.

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Abnormally high thermal conductivity in fivefold twinned diamond nanowires

Cited by 14 publications

References 57 publications

Thermal conductivity of fivefold twinned silicon-germanium heteronanowires

Thermal conductivity of fivefold twinned silicon-germanium heteronanowires

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

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