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Thermal conductivity prediction by atomistic simulation methods: Recent advances and detailed comparison

Journal of applied physics, 2021-12, Vol.130 (21)

2021

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Improving the accuracy of the neuroevolution machine learning potential for multi-component systems

Journal of physics. Condensed matter, 2022-03, Vol.34 (12), p.125902

Fan, Zheyong

2022

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Accurate prediction of heat conductivity of water by a neuroevolution potential

The Journal of chemical physics, 2023-05, Vol.158 (20)

2023

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

The Journal of chemical physics, 2022-09, Vol.157 (11), p.114801-114801

2022

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Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport

Physical review. B, 2021-09, Vol.104 (10), p.1, Article 104309

2021

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Gradient nano-grained graphene as 2D thermal rectifier: A molecular dynamics based machine learning study

Applied physics letters, 2022-09, Vol.121 (13)

2022

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Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics

The Journal of chemical physics, 2024-07, Vol.161 (1)

2024

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Inter-layer and intra-layer heat transfer in bilayer/monolayer graphene van der Waals heterostructure: Is there a Kapitza resistance analogous?

Applied physics letters, 2018-06, Vol.112 (23)

2018

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Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials

Journal of applied physics, 2024-04, Vol.135 (16)

2024

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Pushing thermal conductivity to its lower limit in crystals with simple structures

Nature communications, 2024-04, Vol.15 (1), p.3007-3007, Article 3007

2024

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Anomalous thermal conductivity enhancement in low dimensional resonant nanostructures due to imperfections

Nanoscale, 2021-06, Vol.13 (22), p.11-115

2021

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Kapitza thermal resistance across individual grain boundaries in graphene

Carbon (New York), 2017-12, Vol.125, p.384-390

2017

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Unification of nonequilibrium molecular dynamics and the mode-resolved phonon Boltzmann equation for thermal transport simulations

Physical review. B, 2020-04, Vol.101 (15), p.1, Article 155308

2020

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Publisher's Note: “Inter-layer and intra-layer heat transfer in bilayer/monolayer graphene van der Waals heterostructure: Is there a Kapitza resistance analogous?” [Appl. Phys. Lett. 112, 233104 (2018)]

Applied physics letters, 2018-08, Vol.113 (7)

2018

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Combining the D3 dispersion correction with the neuroevolution machine-learned potential

Journal of physics. Condensed matter, 2024-03, Vol.36 (12), p.125901

2024

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Erratum: Combining the D3 dispersion correction with the neuroevolution machine-learned potential (2024 J. Phys.: Condens. Matter 36 125901)

Journal of physics. Condensed matter, 2024-03, Vol.36 (22), p.229601

2024

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Energetics and structure of grain boundary triple junctions in graphene

Scientific reports, 2017-07, Vol.7 (1), p.4754-14, Article 4754

2017

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Searching for the best thermoelectrics through the optimization of transport distribution function

Journal of applied physics, 2011-04, Vol.109 (7), p.073713-073713-6

2011

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Efficient molecular dynamics simulations with many-body potentials on graphics processing units

Computer physics communications, 2017-09, Vol.218, p.10-16

2017

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Enhanced thermoelectric performance in graphitic ZnO (0001) nanofilms

Journal of applied physics, 2013-02, Vol.113 (8)

2013

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Enhanced thermoelectric performance in three-dimensional superlattice of topological insulator thin films

Nanoscale research letters, 2012-10, Vol.7 (1), p.570-570, Article 570

2012

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Technical note: TERMA scaling as an effective heterogeneity correction model for convolution‐based external‐beam photon dose calculations

Medical physics (Lancaster), 2023-05, Vol.50 (5), p.3191-3198

2023

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Ultrahigh Convergent Thermal Conductivity of Carbon Nanotubes from Comprehensive Atomistic Modeling

Physical review letters, 2021-07, Vol.127 (2), p.025902-025902, Article 025902

2021

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Methodology Perspective of Computing Thermal Transport in Low-Dimensional Materials and Nanostructures: The Old and the New

ACS omega, 2018-03, Vol.3 (3), p.3278-3284

2018

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Size effect and transient phonon transport mechanism in approach-to-equilibrium molecular dynamics simulations

Physical review. B, 2022-02, Vol.105 (7), Article 075301

2022

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Thermal conductivity prediction by atomistic simulation methods: Recent advances and detailed comparison

Improving the accuracy of the neuroevolution machine learning potential for multi-component systems

Accurate prediction of heat conductivity of water by a neuroevolution potential

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

Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport

Gradient nano-grained graphene as 2D thermal rectifier: A molecular dynamics based machine learning study

Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics

Inter-layer and intra-layer heat transfer in bilayer/monolayer graphene van der Waals heterostructure: Is there a Kapitza resistance analogous?

Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials

Pushing thermal conductivity to its lower limit in crystals with simple structures

Anomalous thermal conductivity enhancement in low dimensional resonant nanostructures due to imperfections

Kapitza thermal resistance across individual grain boundaries in graphene

Unification of nonequilibrium molecular dynamics and the mode-resolved phonon Boltzmann equation for thermal transport simulations

Publisher's Note: “Inter-layer and intra-layer heat transfer in bilayer/monolayer graphene van der Waals heterostructure: Is there a Kapitza resistance analogous?” [Appl. Phys. Lett. 112, 233104 (2018)]

Combining the D3 dispersion correction with the neuroevolution machine-learned potential

Erratum: Combining the D3 dispersion correction with the neuroevolution machine-learned potential (2024 J. Phys.: Condens. Matter 36 125901)

Energetics and structure of grain boundary triple junctions in graphene

Searching for the best thermoelectrics through the optimization of transport distribution function

Efficient molecular dynamics simulations with many-body potentials on graphics processing units

Enhanced thermoelectric performance in graphitic ZnO (0001) nanofilms

Enhanced thermoelectric performance in three-dimensional superlattice of topological insulator thin films

Technical note: TERMA scaling as an effective heterogeneity correction model for convolution‐based external‐beam photon dose calculations

Ultrahigh Convergent Thermal Conductivity of Carbon Nanotubes from Comprehensive Atomistic Modeling

Methodology Perspective of Computing Thermal Transport in Low-Dimensional Materials and Nanostructures: The Old and the New

Size effect and transient phonon transport mechanism in approach-to-equilibrium molecular dynamics simulations

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