相关论文: PYSED: A tool for extracting kinetic-energy-weight…
Probing detailed spectral dependence of phonon transport properties in bulk materials is critical to improve the function and performance of structures and devices in a diverse spectrum of technologies. Currently, such information can only…
The spectral energy density (SED) method is used to obtain the phonon dispersion of materials in molecular dynamics codes, e.g., LAMMPS. We show how the electric analog of the SED method can be done using commercial circuit simulators to…
From nano-scale heat transfer point of view, currently one of the most interesting and challenging tasks is to quantitatively analyzing phonon mode specific transport properties in solid materials, which plays vital role in many emerging…
Predictive modeling of the phonon/thermal transport properties of materials is vital to rational design for a diverse spectrum of engineering applications. Classical Molecular Dynamics (MD) simulations serve as a tool to simulate the time…
We perform molecular dynamics (MD) simulations with phonon spectral analysis aiming at understanding the two dimensional (2D) thermal transport in suspended and supported graphene. Within the framework of equilibrium MD simulations, we…
One central challenge in understanding phonon thermal transport is a lack of experimental tools to investigate mode-based transport information. Although recent advances in computation lead to mode-based information, it is hindered by…
We 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…
A primary challenge when deploying speaker recognition systems in real-world applications is performance degradation caused by environmental mismatch. We propose a diffusion-based method that takes speaker embeddings extracted from a…
Boltzmann transport equation (BTE) is an ideal tool to describe the multiscale phonon transport phenomena, which are critical to applications like microelectronics cooling. Numerically solving phonon BTE is extremely computationally…
Path-integral molecular dynamics (PIMD) simulations are crucial for accurately capturing nuclear quantum effects in materials. However, their computational intensity and reliance on multiple software packages often limit their applicability…
API Phonons is a Python software package to predict the transport dynamics of heat-carrying phonons. Using the powerful syntax of Python, this package provides modules and functions interfacing between different packages for atomistic…
The dynamic mode decomposition (DMD) is a simple and powerful data-driven modeling technique that is capable of revealing coherent spatiotemporal patterns from data. The method's linear algebra-based formulation additionally allows for a…
Phononic properties are commonly studied by calculating force constants using the density functional theory (DFT) simulations. Although DFT simulations offer accurate estimations of phonon dispersion relations or thermal properties, but for…
Metals play a crucial role in heat management in electronic devices, such as integrated circuits, making it vital to understand heat transport in elementary metals and alloys. In this work, we systematically study phonon thermal transport…
Silicon carbide (SiC) is an essential material for next generation semiconductors and components for nuclear plants. It's applications are strongly dependent on its thermal conductivity, which is highly sensitive to microstructures.…
Moving object detection (MOD) in remote sensing is significantly challenged by low resolution, extremely small object sizes, and complex noise interference. Current deep learning-based MOD methods rely on probability density estimation,…
Phase mixing is a fundamental kinetic process that governs dissipation and stability in collisionless plasmas, but its inherent filamentation in velocity space creates major challenges for both high-fidelity simulations and reduced-order…
Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community. PySINDy is a Python package that provides tools for applying the…
Recent developments in many-body potential energy representation via deep learning have brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Here we describe DeePMD-kit, a package written in…
The traditional picture of heat transfer in solids by atomic vibrations, also known as phonons, involves phonons scattering with each other like gas particles and is commonly referred to as the phonon gas model (PGM). This physical picture…