Ultrahigh Convergent Thermal Conductivity of Carbon Nanotubes from Comprehensive Atomistic Modeling

Barbalinardo, Giuseppe; Chen, Zekun; Dong, Hai-Kuan; Fan, Zheyong; Donadio, Davide

doi:10.1103/physrevlett.127.025902

Cited by 26 publications

(15 citation statements)

References 69 publications

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“…These new results are correct and challenge the agreement between BTE (with classical statistics) and molecular dynamics (MD) proposed in Ref. [3], and the statement that κ is finite in the bulk limit.…”

supporting

confidence: 65%

“…Barbalinardo et al Reply: Comment [1] shows that the thermal conductivity (κ) of a (10,0) carbon nanotube (CNT), obtained by inversion of the linearized Boltzmann transport equation (BTE), may not converge when the third-order interatomic force constants (IFC) are computed analytically. Bruns et al [1,2] showed that the lifetimes τ RTA ðqÞ of the acoustic branches, computed in the relaxation time approximation (RTA), obey precise power laws for q → 0, which are violated by the numerical IFCs in our Letter [3]. κ in Ref.…”

mentioning

confidence: 67%

“…[1] is much larger than that in Ref. [3], and does not converge even when 400 q-points are used to integrate the first Brillouin zone. These new results are correct and challenge the agreement between BTE (with classical statistics) and molecular dynamics (MD) proposed in Ref.…”

mentioning

confidence: 70%

“…Figure 1 shows that τ RTA ðqÞ from numerical IFCs with dx ¼ 10 −4 Å are nearly indistinguishable from those obtained from analytical IFCs (in Ref. [3] we used dx > 4 × 10 −6 Å). This result is useful for future works for those cases in which it is not possible to compute IFCs analytically.…”

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confidence: 78%

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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.…”

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confidence: 99%

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

Fan,

Wang,

Ying

et al. 2022

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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 two separate Python packages, gpyumd and calorine, which enable the integration of gpumd into Python workflows.

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Non-Fourier heat transport in nanosystems

et al. 2023

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Energy transfer in small nano-sized systems can be very different from that in their macroscopic counterparts due to reduced dimensionality, interaction with surfaces, disorder, and large fluctuations. Those ingredients may induce non-diffusive heat transfer that requires to be taken into account on small scales. We provide an overview of the recent advances in this field from the points of view of nonequilibrium statistical mechanics and atomistic simulations. We summarize the underlying basic properties leading to violations of the standard diffusive picture of heat transport and its universal features, with some historical perspective. We complete this scenario by illustrating also the effects of long-range interaction and integrability on non-diffusive transport. Then we discuss how all of these features can be exploited for thermal management, rectification and to improve the efficiency of energy conversion. We conclude with a review on recent achievements in atomistic simulations of anomalous heat transport in single polymers, nanotubes and two-dimensional materials. A short account of the existing experimental literature is also given.

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Ultrahigh Convergent Thermal Conductivity of Carbon Nanotubes from Comprehensive Atomistic Modeling

Cited by 26 publications

References 69 publications

Barbalinardo et al. Reply:

Barbalinardo et al. Reply:

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

Non-Fourier heat transport in nanosystems

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