The potential widespread adoption of thermoelectric (TE) technology in energy harvesting
applications hinges upon the high-performance, reliable, and cost-effective module …

Over the past few decades, molecular dynamics simulations and first-principles calculations
have become two major approaches to predict the lattice thermal conductivity (κ L), which …

The process of material discovery and design can be simplified and accelerated if we can
effectively learn from existing data. In this study, we explore the use of machine learning …

Existing machine learning potentials for predicting phonon properties of crystals are typically
limited on a material-to-material basis, primarily due to the exponential scaling of model …

Stretchable and conductive nanocomposites are emerging as important constituents of soft
mechanical sensors for health monitoring, human–machine interactions, and soft robotics …

We investigated the accelerated prediction of the thermal conductivity of materials through
end-to-end structure-based approaches employing machine learning methods. Due to the …

Half-Heusler compounds are promising candidates for thermoelectrics. The exploration of
multiphonon interaction, including four-phonon interaction, in half-Heusler compounds …

Low lattice thermal conductivity is essential for high thermoelectric performance of a
material. Lattice thermal conductivity is often computed using density functional theory …

Thermoelectric materials can be used to construct devices which recycle waste heat into
electricity. However, the best known thermoelectrics are based on rare, expensive or even …

Thermal conductivity (TC) is greatly influenced by the working temperature, microstructures,
thermal processing (heat treatment) history and the composition of alloys. Due to …