Related papers: Hyperparameter Optimization and Force Error Correc…
Next-generation high-power radio-frequency (RF) devices increasingly demand transistors that operate efficiently with high gain at high frequencies. High-aluminum-content ultra-wide-bandgap (UWBG) AlGaN alloys have shown great potential for…
Using molecular dynamics(MD) simulations, we study thermal transport in graphene nanoribbons (GNR) subjected to uniform uniaxial and nonuniform strain fields. We predict significant thermal rectification (over 70%) in a rectangular armchair…
Knowledge of thermal properties is essential to design and evaluate thermal systems and processes using nanofluids. This paper presents different analytical models to predict thermal conductivity and viscosity. The efforts have been made to…
We propose an approach that can accurately predict the heat conductivity of liquid water. On the one hand, we develop an accurate machine-learned potential based on the neuroevolution-potential approach that can achieve quantum-mechanical…
Transmission lines are vital components in power systems. Tripping of transmission lines caused by over-temperature is a major threat to the security of system operations, so it is necessary to efficiently simulate line temperature under…
The $\text{Cu}_7\text{P}\text{S}_6$ compound has garnered significant attention due to its potential in thermoelectric applications. In this study, we introduce a neuroevolution potential (NEP), trained on a dataset generated from ab initio…
Self-heating in next-generation, high-power-density field-effect transistor limits performance and complicates fabrication. Here, we introduce NEP-FET, a machine-learned framework for device-scale heat transport simulations of field-effect…
This paper presents a method to simulate the thermal behavior of 3D systems using a graph neural network. The method discussed achieves a significant speed-up with respect to a traditional finite-element simulation. The graph neural network…
GaN-based HEMTs have the potential to be widely used in high-power and high-frequency electronics while their maximum output powers are limited by high channel temperature induced by near-junction Joule-heating, which degrades device…
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 is trained using reference data from…
In this work we present a molecular dynamics investigation of thermal transport in a silica-gallium nitride nanocomposite. A surprising enhancement of the thermal conductivity for crystalline volume fractions larger than 5% is found, which…
Recently, surrogate models based on deep learning have attracted much attention for engineering analysis and optimization. As the construction of data pairs in most engineering problems is time-consuming, data acquisition is becoming the…
Thick metamorphic buffers are perceived to be indispensable for the heteroepitaxial integration of III-V semiconductors on silicon substrates with large thermal expansion and lattice mismatches. However, III-nitride buffers in conventional…
We determine atomic and electronic structure, formation energy, stability and magnetic properties of native point defects, such as Gallium (Ga) and Nitrogen (N) vacancies in bulk and at non-polar (10$\overline{1}$0) surface of wurtzite…
Metals play a crucial role in heat management in electronic devices, such as integrated circuits, making it vital to understand heat transport in elementary metals and alloys. In this work, we systematically study phonon thermal transport…
Porous nanowires (NWs) with tunable thermal conductance are examined as a candidate for thermoelectric (TE) devices with high efficiency (ZT). Thermal conductance of porous Si and Ge NWs is calculated using the complete phonon dispersion…
Polarization arising from non-centrosymmetric wurtzite lattice underpins the physics and functionality of gallium nitride (GaN)-the most produced semiconductor materials second only to silicon. However, recent direct experimental…
Penta-graphene (PG) has been identified as a novel 2D material with an intrinsic bandgap, which makes it especially promising for electronics applications. In this work, we use first-principles lattice dynamics and iterative solution of the…
The lattice thermal conductivity (LTC) of Ga$_2$O$_3$ is an important property due to the challenge in the thermal management of high-power devices. We develop machine-learned neuroevolution potentials for single-crystalline…
The Generative Adversarial Network (GAN) is a powerful and flexible tool that can generate high-fidelity synthesized data by learning. It has seen many applications in simulating events in High Energy Physics (HEP), including simulating…