### Browsing by Author "Dong, Haikuan"

Now showing 1 - 11 of 11

###### Results Per Page

###### Sort Options

Item Barbalinardo et al. Reply(American Physical Society, 2022-06-24) Barbalinardo, Giuseppe; Chen, Zekun; Dong, Haikuan; Fan, Zheyong; Donadio, Davide; University of California Davis; Multiscale Statistical and Quantum Physics; Department of Applied PhysicsItem Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials(Institute of Physics Publishing, 2024-06-19) Fan, Zheyong; Xiao, Yang; Wang, Yanzhou; Ying, Penghua; Chen, Shunda; Dong, Haikuan; Department of Applied Physics; Multiscale Statistical and Quantum Physics; Bohai University; Department of Applied Physics; Tel Aviv University; George Washington UniversityWe propose an efficient approach for simultaneous prediction of thermal and electronic transport properties in complex materials. Firstly, a highly efficient machine-learned neuroevolution potential (NEP) is trained using reference data from quantum-mechanical density-functional theory calculations. This trained potential is then applied in large-scale molecular dynamics simulations, enabling the generation of realistic structures and accurate characterization of thermal transport properties. In addition, molecular dynamics simulations of atoms and linear-scaling quantum transport calculations of electrons are coupled to account for the electron-phonon scattering and other disorders that affect the charge carriers governing the electronic transport properties. We demonstrate the usefulness of this unified approach by studying electronic transport in pristine graphene and thermoelectric transport properties of a graphene antidot lattice, with a general-purpose NEP developed for carbon systems based on an extensive dataset.Item Equivalence of the equilibrium and the nonequilibrium molecular dynamics methods for thermal conductivity calculations: From bulk to nanowire silicon(2018-03-26) Dong, Haikuan; Fan, Zheyong; Shi, Libin; Harju, Ari; Ala-Nissila, Tapio; Department of Applied Physics; Centre of Excellence in Quantum Technology, QTF; Multiscale Statistical and Quantum Physics; Bohai UniversityMolecular dynamics (MD) simulations play an important role in studying heat transport in complex materials. The lattice thermal conductivity can be computed either using the Green-Kubo formula in equilibrium MD (EMD) simulations or using Fourier's law in nonequilibrium MD (NEMD) simulations. These two methods have not been systematically compared for materials with different dimensions and inconsistencies between them have been occasionally reported in the literature. Here we give an in-depth comparison of them in terms of heat transport in three allotropes of Si: three-dimensional bulk silicon, two-dimensional silicene, and quasi-one-dimensional silicon nanowire. By multiplying the correlation time in the Green-Kubo formula with an appropriate effective group velocity, we can express the running thermal conductivity in the EMD method as a function of an effective length and directly compare it to the length-dependent thermal conductivity in the NEMD method. We find that the two methods quantitatively agree with each other for all the systems studied, firmly establishing their equivalence in computing thermal conductivity.Item GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations(American Institute of Physics, 2022-09-21) Fan, Zheyong; Wang, Yanzhou; Ying, Penghua; Song, Keke; Wang, Junjie; Wang, Yong; Zeng, Zezhu; Xu, Ke; Lindgren, Eric; Rahm, J. Magnus; Gabourie, Alexander J.; Liu, Jiahui; Dong, Haikuan; Wu, Jianyang; Chen, Yue; Zhong, Zheng; Sun, Jian; Erhart, Paul; Su, Yanjing; Ala-Nissila, Tapio; Department of Applied Physics; Multiscale Statistical and Quantum Physics; Multiscale Statistical and Quantum Physics; Harbin Institute of Technology; University of Science and Technology Beijing; Nanjing University; The University of Hong Kong; Xiamen University; Chalmers University of Technology; Stanford University; Bohai UniversityWe 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.Item Heat transport across graphene/hexagonal-BN tilted grain boundaries from phase-field crystal model and molecular dynamics simulations(American Institute of Physics, 2021-12-21) Dong, Haikuan; Hirvonen, Petri; Fan, Zheyong; Qian, Ping; Su, Yanjing; Ala-Nissila, Tapio; Centre of Excellence in Quantum Technology, QTF; University of Science and Technology Beijing; Department of Applied PhysicsWe 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.Item Homogeneous nonequilibrium molecular dynamics method for heat transport and spectral decomposition with many-body potentials(American Physical Society, 2019-02-28) Fan, Zheyong; Dong, Haikuan; Harju, Ari; Ala-Nissilä, Tapio; Department of Applied Physics; Centre of Excellence in Quantum Technology, QTF; Multiscale Statistical and Quantum Physics; Bohai UniversityThe standard equilibrium Green-Kubo and nonequilibrium molecular dynamics (MD) methods for computing thermal transport coefficients in solids typically require relatively long simulation times and large system sizes. To this end, we revisit here the homogeneous nonequilibrium MD method by Evans [Phys. Lett. A 91, 457 (1982)PYLAAG0375-960110.1016/0375-9601(82)90748-4] and generalize it to many-body potentials that are required for more realistic materials modeling. We also propose a method for obtaining spectral conductivity and phonon mean-free path from the simulation data. This spectral decomposition method does not require lattice dynamics calculations and can find important applications in spatially complex structures. We benchmark the method by calculating thermal conductivities of three-dimensional silicon, two-dimensional graphene, and a quasi-one-dimensional carbon nanotube and show that the method is about one to two orders of magnitude more efficient than the Green-Kubo method. We apply the spectral decomposition method to examine the long-standing dispute over thermal conductivity convergence vs divergence in carbon nanotubes.Item Interpretation of apparent thermal conductivity in finite systems from equilibrium molecular dynamics simulations(American Physical Society, 2021-01-19) Dong, Haikuan; Xiong, Shiyun; Fan, Zheyong; Qian, Ping; Su, Yanjing; Ala-Nissila, Tapio; Department of Applied Physics; Multiscale Statistical and Quantum Physics; Centre of Excellence in Quantum Technology, QTF; Soochow University; University of Science and Technology BeijingWe 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.Item Molecular dynamics simulations of heat transport using machine-learned potentials : A mini-review and tutorial on GPUMD with neuroevolution potentials(American Institute of Physics, 2024-04-28) Dong, Haikuan; Shi, Yongbo; Ying, Penghua; Xu, Ke; Liang, Ting; Wang, Yanzhou; Zeng, Zezhu; Wu, Xin; Zhou, Wenjiang; Xiong, Shiyun; Chen, Shunda; Fan, Zheyong; Department of Applied Physics; Centre of Excellence in Quantum Technology, QTF; Multiscale Statistical and Quantum Physics; Bohai University; Tel Aviv University; Chinese University of Hong Kong; Centre of Excellence in Quantum Technology, QTF; Institute of Science and Technology Austria; South China University of Technology; Peking University; Guangdong University of Technology; George Washington UniversityMolecular dynamics (MD) simulations play an important role in understanding and engineering heat transport properties of complex materials. An essential requirement for reliably predicting heat transport properties is the use of accurate and efficient interatomic potentials. Recently, machine-learned potentials (MLPs) have shown great promise in providing the required accuracy for a broad range of materials. In this mini-review and tutorial, we delve into the fundamentals of heat transport, explore pertinent MD simulation methods, and survey the applications of MLPs in MD simulations of heat transport. Furthermore, we provide a step-by-step tutorial on developing MLPs for highly efficient and predictive heat transport simulations, utilizing the neuroevolution potentials as implemented in the GPUMD package. Our aim with this mini-review and tutorial is to empower researchers with valuable insights into cutting-edge methodologies that can significantly enhance the accuracy and efficiency of MD simulations for heat transport studies.Item Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport(American Physical Society, 2021-09-18) Fan, Zheyong; Zeng, Zezhu; Zhang, Cunzhi; Wang, Yanzhou; Song, Keke; Dong, Haikuan; Chen, Yue; Ala-Nissila, Tapio; Department of Applied Physics; Centre of Excellence in Quantum Technology, QTF; Multiscale Statistical and Quantum Physics; The University of Hong Kong; University of Chicago; Multiscale Statistical and Quantum Physics; University of Science and Technology BeijingWe develop a neuroevolution-potential (NEP) framework for generating neural network-based machine-learning potentials. They are trained using an evolutionary strategy for performing large-scale molecular dynamics (MD) simulations. A descriptor of the atomic environment is constructed based on Chebyshev and Legendre polynomials. The method is implemented in graphic processing units within the open-source gpumd package, which can attain a computational speed over atom-step per second using one Nvidia Tesla V100. Furthermore, per-atom heat current is available in NEP, which paves the way for efficient and accurate MD simulations of heat transport in materials with strong phonon anharmonicity or spatial disorder, which usually cannot be accurately treated either with traditional empirical potentials or with perturbative methods.Item Phase-field crystal model for heterostructures(American Physical Society, 2019-10-16) Hirvonen, Petri; Heinonen, Vili; Dong, Haikuan; Fan, Zheyong; Elder, Ken R.; Ala-Nissila, Tapio; Centre of Excellence in Quantum Technology, QTF; Massachusetts Institute of Technology MIT; Bohai University; Oakland University; Department of Applied PhysicsAtomically thin two-dimensional heterostructures are a promising, novel class of materials with ground-breaking properties. The possibility of choosing many constituent components and their proportions allows optimization of these materials to specific requirements. The wide adaptability comes with a cost of large parameter space making it hard to experimentally test all the possibilities. Instead, efficient computational modeling is needed. However, large range of relevant time and length scales related to physics of polycrystalline materials poses a challenge for computational studies. To this end, we present an efficient and flexible phase-field crystal model to describe the atomic configurations of multiple atomic species and phases coexisting in the same physical domain. We extensively benchmark the model for two-dimensional binary systems in terms of their elastic properties and phase boundary configurations and their energetics. As a concrete example, we demonstrate modeling lateral heterostructures ofgraphene and hexagonal boron nitride. We consider both idealized bicrystals and large-scale systems with random phase distributions. We find consistent relative elastic moduli and lattice constants, as well as realistic continuous interfaces and faceted crystal shapes. Zigzag-oriented interfaces are observed to display the lowest formation energy.Item Ultrahigh Convergent Thermal Conductivity of Carbon Nanotubes from Comprehensive Atomistic Modeling(American Physical Society, 2021-07-09) Barbalinardo, Giuseppe; Chen, Zekun; Dong, Haikuan; Fan, Zheyong; Donadio, Davide; Department of Applied Physics; Multiscale Statistical and Quantum Physics; Centre of Excellence in Quantum Technology, QTF; University of California, DavisAnomalous heat transport in one-dimensional nanostructures, such as nanotubes and nanowires, is a widely debated problem in condensed matter and statistical physics, with contradicting pieces of evidence from experiments and simulations. Using a comprehensive modeling approach, comprised of lattice dynamics and molecular dynamics simulations, we proved that the infinite length limit of the thermal conductivity of a (10,0) single-wall carbon nanotube is finite but this limit is reached only for macroscopic lengths due to a thermal phonon mean free path of several millimeters. Our calculations showed that the extremely high thermal conductivity of this system at room temperature is dictated by quantum effects. Modal analysis showed that the divergent nature of thermal conductivity, observed in one-dimensional model systems, is suppressed in carbon nanotubes by anharmonic scattering channels provided by the flexural and optical modes with polarization in the plane orthogonal to the transport direction.