General-purpose machine-learned potential for 16 elemental metals and their alloys.

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a promising approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices.

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.

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 (Dong2021B035417) 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.

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.

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 (β) → cubic (α) is observed during the heating process.

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

Recently, novel 2D InGeTe has been successfully synthesized and attracted attention due to its excellent properties. In this study, we investigated the mechanical properties and transport behavior of InGeX (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.

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

In the breakthrough progress made in the latest experiment Hou(2022507), 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.

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.

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.

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.

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.

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.