Thermal conductivity reduction in carbon nanotube by fullerene encapsulation: A molecular dynamics study

Single-walled carbon nanotubes (SWCNTs) in their pristine form have high thermal conductivity whose further improvement has attracted a lot of interest. Some theoretical studies have suggested that the thermal conductivity of a (10,10) SWCNT is dramatically enhanced by C60 fullerene encapsulation. However, recent experiments on SWCNT bundles show that fullerene encapsulation leads to a reduction rather than an increase in thermal conductivity. Here, we employ three different molecular dynamics methods to study the influence of C60 encapsulation on heat transport in a (10,10) SWCNT. All the three methods consistently predict a reduction of the thermal conductivity of (10,10) SWCNT upon C60 encapsulation by 20% - 30%, in agreement with experimental results on bundles of SWCNTs. We demonstrate that there is a simulation artifact in the Green-Kubo method which gives anomalously large thermal conductivity from artificial convection. Our results show that the C60 molecules conduct little heat compared to the outer SWCNT and reduce the phonon mean free paths of the SWCNT by inducing extra phonon scattering. We also find that the thermal conductivity of a (10,10) SWCNT monotonically decreases with increasing filling ratio of C60 molecules

Heat transport across graphene/hexagonal-BN tilted grain boundaries from phase-field crystal model and molecular dynamics simulations

We study the interfacial thermal conductance of grain boundaries (GBs) between monolayer graphene and hexagonal boron nitride (h-BN) sheets using a combined atomistic approach. First, realistic samples containing graphene/h-BN GBs with different tilt angles are generated using the phase-field crystal model developed recently [P. Hirvonen et al., Phys. Rev. B 100, 165412 (2019)] that captures slow diffusive relaxation inaccessible to molecular dynamics (MD) simulations. Then, large-scale MD simulations using the efficient GPUMD package are performed to assess heat transport and rectification properties across the GBs. We find that lattice mismatch between the graphene and h-BN sheets plays a less important role in determining the interfacial thermal conductance as compared to the tilt angle. In addition, we find no significant thermal rectification effects for these GBs

Interpretation of apparent thermal conductivity in finite systems from equilibrium molecular dynamics simulations

We propose a way to properly interpret the apparent thermal conductivity obtained for finite systems using equilibrium molecular dynamics simulations (EMD) with fixed or open boundary conditions in the transport direction. In such systems the heat current autocorrelation function develops negative values after a correlation time which is proportional to the length of the simulation cell in the transport direction. Accordingly, the running thermal conductivity develops a maximum value at the same correlation time and eventually decays to zero. By comparing EMD with nonequilibrium molecular dynamics (NEMD) simulations, we conclude that the maximum thermal conductivity from EMD in a system with domain length 2L is equal to the thermal conductivity from NEMD in a system with domain length L. This facilitates the use of nonperiodic-boundary EMD for thermal transport in finite samples in close correspondence to NEMD

A minimal Tersoff potential for diamond silicon with improved descriptions of elastic and phonon transport properties

Silicon is an important material and many empirical interatomic potentials have been developed for atomistic simulations of it. Among them, the Tersoff potential and its variants are the most popular ones. However, all the existing Tersoff-like potentials fail to reproduce the experimentally measured thermal conductivity of diamond silicon. Here we propose a modified Tersoff potential and develop an efficient open source code called GPUGA (graphics processing units genetic algorithm) based on the genetic algorithm and use it to fit the potential parameters against energy, virial and force data from quantum density functional theory calculations. This potential, which is implemented in the efficient open source GPUMD (graphics processing units molecular dynamics) code, gives significantly improved descriptions of the thermal conductivity and phonon dispersion of diamond silicon as compared to previous Tersoff potentials and at the same time well reproduces the elastic constants. Furthermore, we find that quantum effects on the thermal conductivity of diamond silicon at room temperature are non-negligible but small: Using classical statistics underestimates the thermal conductivity by about 10% as compared to using quantum statistics

Efficient Excitation and Active Control of Propagating Graphene Plasmons with a Spatially Engineered Graphene Nanoantenna

Graphene plasmons (GPs) are of great importance in photonics and optoelectronics due to ultrahigh near-field confinement and enhancement. However, the large momentum mismatch between GPs and incident light hinders the efficient excitation of GPs. Conventional excitation schemes, such as prism coupling, grating coupling, and resonant metal antennae, go against the tunability and multifunction of the GP device. Here, we numerically demonstrate the efficient excitation and active control of propagating GPs in a resonant graphene nanoantenna (GNA)-based GP launcher. The resonant GNA provides high-momentum near-field components to match the wavevector of GPs, and the excitation efficiency is significantly enhanced by the quarter-wavelength condition in a reflective configuration. Furthermore, the propagating behavior of GPs is gate-tunable with a GNA. Using spatially engineered GNAs, a tunable directional GP launcher with an extinction ratio of larger than 1000 is achieved. Moreover, we design a vertically crossed GNA-based propagating GP launcher that can serve as the incident polarization information recording. Finally, some graphene plasmonic circuits at the nanoscale, such as a GP waveguide, splitter, and prism, are realized using spatial conductivity patterns in graphene. The efficient excitation and flexible control of propagating GPs with engineered GNAs associated with the spatial conductivity patterns in graphene provide a gate-tunable and multifunctional platform for nanoscale graphene plasmonic devices

Anomalous Frictional Behaviors of Ir and Au Tips Sliding on Graphene/Ni(111) Substrate: Density Functional Theory Calculations

The atomic force microscope (AFM) provides a facilitating tool to investigate the atomic-scale friction properties of surfaces through the sliding of the scanning tip; therefore, the interaction between the tip and the surface should play an important role to determine the frictional behaviors. In this study, density functional theory (DFT) calculations have been carried out to perform the pushing down processes of a tip (10 atom Ir or Au tip) on the top, hollow, and bridge sites of the graphene/Ni(111) substrate. The calculation results indicate that the interactions between the tips and the graphene/Ni(111) substrate influence the adsorption energy remarkably, leading to the sequence of bridge < top < hollow for Ir and Au tips, which is totally different from the adsorption energy of an inert Ar atom, following the sequence of hollow < bridge < top. The strong interactions between the (Ir or Au) tip and the graphene/Ni(111) substrate will introduce novel frictional properties into the system, and an anomalous negative friction coefficient could be obtained. Further investigations show that these interactions arise from the hybridizations between the 2p_<i>z</i> orbitals of C atoms and the 5d_<i>z</i>² orbitals of the tip apex atom

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

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 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, viz., gpyumd, calorine, and pynep, that enable the integration of gpumd into Python workflows