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Tungsten (W) is a material of choice for the divertor material due to its high melting temperature, thermal conductivity, and sputtering threshold. However, W has a very high brittle-to-ductile transition temperature and at fusion reactor…
Neural networks (NNs) are increasingly used for data-driven subgrid-scale parameterization in weather and climate models. While NNs are powerful tools for learning complex nonlinear relationships from data, there are several challenges in…
Nanoscale hot spots forming tens of nanometers beneath the gate in advanced FinFET and HEMT devices drive heat transport into a non-Fourier regime, challenging conventional (Fourier-based) finite-element (FEM) analyses and complicating…
With the advent of system-in-package (SiP) chiplet-based design and heterogeneous 2.5D/3D integration, thermal-induced warpage has become a critical reliability concern. While conventional numerical approaches can deliver highly accurate…
High-resolution (HR) precipitation prediction is essential for reducing damage from stationary and localized heavy rainfall; however, HR precipitation forecasts using process-driven numerical weather prediction models remains challenging.…
Patterned-transducer thermoreflectance enhances sensitivity to low-thermal-conductivity materials by suppressing lateral heat spreading in the metal transducer, but its wider use is limited by the cost of repeated high-fidelity forward…
Past molecular dynamics studies of thermal transport have predominantly used Stillinger-Weber potentials. As materials continuously shrink, their properties increasingly depend on defect and surface effects. Unfortunately, Stillinger-Weber…
Non-equilibrium molecular dynamics (NEMD) techniques are widely used for investigating lattice thermal conductivity. Recently, machine learning force fields (MLFFs) have emerged as a promising approach to enhance the precision in NEMD…
Boltzmann transport calculations based on band structures computed from first principles play an important role in modern thermoelectric materials research. Among available codes, the \textsc{BoltzTraP} code is the most widely adopted, but…
We demonstrate a substrate-supported thermometry platform to measure thermal conduction in nanomaterials like graphene, with no need to suspend them. We use three-dimensional simulations and careful uncertainty analysis to optimize the…
GaN is a wide-bandgap semiconductor used in high-efficiency LEDs and solar cells. The solid is produced industrially at high chemical purities by deposition from a vapour phase, and oxygen may be included at this stage. Oxidation represents…
Low dimensional structures have demonstrated improved thermoelectric (TE) performance because of a drastic reduction in their thermal conductivity, {\kappa}l. This has been observed for a variety of materials, even for traditionally poor…
In the present study, the capabilities of a new Convolutional Neural Network (CNN) model are explored with the paramount objective of reconstructing the temperature field of wall-bounded flows based on a limited set of measurement points…
We combine machine learning (ML)-based neuroevolution potentials (NEP) with anharmonic lattice dynamics and the Boltzmann transport equation (ALD-BTE) to achieve a quantitative and mode-resolved description of thermal transport in…
Thermal transport in low-dimensional semiconductors is crucial for advancing thermal management in nanoelectronics, quantum devices, and thermoelectric devices. Recent molecular dynamics (MD) studies have identified a nonmonotonic…
As electricity grids become more renewable energy-compliant, there will be a need for novel semiconductors that can withstand high power, high voltage, and high temperatures. Wide band gap (WBG) semiconductors tend to exhibit large…
A machine-learning non-contact method to determine the temperature of a laser gain medium via its laser emission with a trained few-layer neural net model is presented. The training of the feed-forward Neural Network (NN) enables the…
High-density through-substrate vias (TSVs) enable 2.5D/3D heterogeneous integration but introduce significant signal-integrity and thermal-reliability challenges due to electrical coupling, insertion loss, and self-heating. Conventional…
With electric power systems becoming more compact and increasingly powerful, the relevance of thermal stress especially during overload operation is expected to increase ceaselessly. Whenever critical temperatures cannot be measured…
Understanding heat transport at the nanometer scale is critical for semiconductor devices, quantum materials, and thermal management of nanostructures, yet direct local measurements of thermal conductivity and heat capacity remain scarce.…