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

Machine learned potentials (MLPs) have been widely employed in molecular dynamics simulations to study thermal transport. However, the literature results indicate that MLPs generally underestimate the lattice thermal conductivity (LTC) of typical solids. Here, we quantitatively analyze this underestimation in the context of the neuroevolution potential (NEP), which is a representative MLP that balances efficiency and accuracy. Taking crystalline silicon, gallium arsenide, graphene, and lead telluride as examples, we reveal that the fitting errors in the machine-learned forces against the reference ones are responsible for the underestimated LTC as they constitute external perturbations to the interatomic forces. Since the force errors of a NEP model and the random forces in the Langevin thermostat both follow a Gaussian distribution, we propose an approach to correcting the LTC by intentionally introducing different levels of force noises via the Langevin thermostat and then extrapolating to the limit of zero force error. Excellent agreement with experiments is obtained by using this correction for all the prototypical materials over a wide range of temperatures. Based on spectral analyses, we find that the LTC underestimation mainly arises from increased phonon scatterings in the low-frequency region caused by the random force errors.

Thermal conductivity of irregularly shaped nanoparticles from equilibrium molecular dynamics.

The computation of thermal conductivity for finite nanoparticulate systems, particularly those of irregular shapes, poses significant challenges. The nonequilibrium molecular dynamics (NEMD) methods has been extensively utilized in numerous prior studies for the computation of thermal conductivity of nanoparticles. One of our recent works (Donget al2021Phys. Rev.B103035417) proposed that equilibrium molecular dynamics (EMD) methods can be used for the simulation of thermal conductivity of finite-scale systems and demonstrated their equivalence to NEMD methods. In this study, we investigated the application of the (EMD) approach for the computation of thermal conductivity in zero-dimensional nanoparticles. In our initial step, we merged both methodologies to substantiate the equivalence in thermal conductivity calculation for cube and cylinder nanoparticles. After filtering the data, we confirmed the usefulness of EMD for evaluating the thermal conductivity of zero-dimensional materials. The NEMD method faces challenges in accurately predicting thermal conductivity in nanoparticle systems with a varying cross-sectional area along the transport direction, whereas EMD methods can be utilized to estimate thermal conductivity when the volume is known. In a subsequent study, we used the state-of-the-art machine learning potential to calculate the thermal conductivity of spherical nanoparticles and compared the results with those obtained using the classical Tersoff potential. Ultimately, we predicted the thermal conductivity of nanoparticles with various geometries in all directions. Our findings collectively demonstrate the simplicity and effectiveness of employing EMD methods for calculating thermal conductivity in nanoparticle systems, thereby opening up new avenues for investigating thermal transport properties in particle systems as well as nanopders.

Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials.

We 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.

Investigation of phase transition, mechanical behavior and lattice thermal conductivity of halogen perovskites using machine learning interatomic potentials.

Using a machine learning (ML) approach to fit DFT data, interatomic potentials have been successfully extracted. In this study, the phase transition, mechanical behavior and lattice thermal conductivity are investigated for halogen perovskites using NEP-based MD simulations in a large supercell including 16 000 atoms, which breaks through the size and temperature effects in DFT. A clear phase transition from orthorhombic (γ) → tetragonal (ß) → cubic (α) is observed during the heating process. During the cooling process, CsPbCl3 and CsPbBr3 exhibit perfect reversible behavior, while CsPbI3 only undergoes a phase transition from α to ß. Then, the key mechanical parameters, including Poisson's ratio, tensile strength, critical strain and bulk modulus, are predicted. The thermal conductivity is also investigated using the NEP-based MD simulations. At room temperature, they exhibit extremely low thermal conductivity. The predicted results are compared with the experimental results, and the rationality of ML potentials has been confirmed.

Investigation of the mechanical and transport properties of InGeX3 (X = S, Se and Te) monolayers using density functional theory and machine learning.

Recently, novel 2D InGeTe3 has been successfully synthesized and attracted attention due to its excellent properties. In this study, we investigated the mechanical properties and transport behavior of InGeX3 (X = S, Se and Te) monolayers using density functional theory (DFT) and machine learning (ML). The key physical parameters related to mechanical properties, including Poisson's ratio, elastic modulus, tensile strength and critical strain, were revealed. Using a ML method to train DFT data, we developed a neuroevolution-potential (NEP) to successfully predict the mechanical properties and lattice thermal conductivity. The fracture behavior predicted using NEP-based MD simulations in a large supercell containing 20 000 atoms could be verified using DFT. Due to the effects of size, these predicted physical parameters have a slight difference between DFT and ML methods. At 300 K, these monolayers exhibited a low thermal conductivity with the values of 13.27 ± 0.24 W m-1 K-1 for InGeS3, 7.68 ± 0.30 W m-1 K-1 for InGeSe3, and 3.88 ± 0.09 W m-1 K-1 for InGeTe3, respectively. The Boltzmann transport equation (BTE) including all electron-phonon interactions was used to accurately predict the electron mobility. Compared with InGeS3 and InGeSe3, the InGeTe3 monolayer showed flexible mechanical behavior, low thermal conductivity and high mobility.

A first-principles study of 1D and 2D C60nanostructures: strain effects on band alignments and carrier mobility.

In the breakthrough progress made in the latest experiment Houet al(2022Nature606507), 2DC60polymer was exfoliated from the quasi-hexagonal bulk crystals. BulkC60polymer with quasi-tetragonal phase was found to easily form 1D fullerene structure withC60molecules connected by C=C. Inspired by the experiment, we investigate the strain behaviors of 1D and 2DC60polymers by first-principles calculations. Some physical properties of these low dimensionalC60polymers, including structural stability, elastic behavior, band alignment and carrier mobility, are predicted. Compared with fullereneC60molecule, 1D and 2DC60polymers are metastable. At absolute zero temperature, 1DC60bears a uniaxial tensile strain less than 11.5%, and 2D monolayerC60withstands a biaxial tensile strain less than 7.5%. At 300 K,ab initiomolecular dynamics confirm that they can withstand the strains of 9% and 5%, respectively. Strain engineering can adjust the absolute position of the band edge. In the absence of strain, carrier mobility is predicted to beµe= 398 andµh= 322cm2V-1s-1for 1DC60polymer, andµe,x=74/µe,y= 34cm2V-1s-1andµh,x=646/µh,y= 1487cm2V-1s-1for 2DC60polymer. Compared with other carbon based semiconductors, theseC60polymers exhibit high effective mass, resulting in low mobility.

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.

Ultrahigh Convergent Thermal Conductivity of Carbon Nanotubes from Comprehensive Atomistic Modeling.

Anomalous 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.

Polarization Toffoli gate assisted by multiple degrees of freedom.

A Toffoli gate plays a critical role in many quantum algorithms due to its function as a building block, which is a fundamental element for feasible large-scale quantum computation. With the help of polarization, spatial, and temporal degrees of freedom (DOFs), a construction scheme of a nearly deterministic polarization Toffoli gate is proposed, where only two two-photon gates are required. The simple construction circuit together with available techniques and optical elements facilitate the realization of the scheme presented here. This construction scheme can be utilized as a reference for multiqubit quantum gates with multiple DOFs.

Heat transport in pristine and polycrystalline single-layer hexagonal boron nitride.

We use a phase field crystal model to generate large-scale bicrystalline and polycrystalline single-layer hexagonal boron nitride (h-BN) samples and employ molecular dynamics (MD) simulations with the Tersoff many-body potential to study their heat transport properties. The Kapitza thermal resistance across individual h-BN grain boundaries is calculated using the inhomogeneous nonequilibrium MD method. The resistance displays strong dependence on the tilt angle, the line tension and the defect density of the grain boundaries. We also calculate the thermal conductivity of pristine h-BN and polycrystalline h-BN with different grain sizes using an efficient homogeneous nonequilibrium MD method. The in-plane and the out-of-plane (flexural) phonons exhibit different grain size scalings of the thermal conductivity in polycrystalline h-BN and the extracted Kapitza conductance is close to that of large-tilt-angle grain boundaries in bicrystals.

Fault-tolerant distribution of GHZ states and controlled DSQC based on parity analyses.

Based on the circuit including linear optical elements, a fault-tolerant distribution of GHZ states against collective noise among three parties is proposed. Additionally, two controlled DSQC protocols using the shared GHZ states as quantum channels are also presented under the charge of the controller. The first controlled DSQC protocol applies single parity analysis based on weak cross-Kerr nonlinearities. The receiver Bob performs single-photon measurement to obtain the secret information after the outcome publication of the single parity analysis executed by the sender Alice. The second protocol applies dense coding to double information transmission capacity, and the double parity analyses based on weak cross-Kerr nonlinearities are performed to obtain the secret information.

Single logical qubit information encoding scheme with the minimal optical decoherence-free subsystem.

We present a scheme for encoding single logical qubit information, which is immune to collective decoherence acting on Hilbert space spanned by the corresponding states. The scheme needs a spatial entanglement gate and a polarization entanglement gate, which are realized with the assistance of weak cross-Kerr nonlinear interaction between photons and coherent states via Kerr media. Under the condition of sufficient large phase shifts, single logical qubit information can be encoded into this minimal optical decoherence-free subsystem with near-unity fidelity. Together with the mature techniques of measurement and classical feed forward, simple linear optical elements are applied to complete the encoding task, which offers the feasibility of this scheme for protecting quantum information against decoherence.