Advances in machine learning have led to the development of foundation models for
atomistic materials chemistry, enabling quantum-accurate descriptions of interatomic forces …

This paper introduces the infrared crystal phase, and provides unconventional mechanistic
insights into the commonly thought “crystal phase”. The critical challenge with the obtained …

Thermal conductivity is a key thermophysical property governing the heat transport in
materials. Specifically, some applications such as thermoelectrics and thermal coatings …

To address the challenges of high error rates and poor generalization in current deep
learning models for predicting lattice thermal conductivity (LTC), we introduce …

Abstract Machine learning has demonstrated superior performance in predicting vast
materials properties. However, predicting a spectral-like continuous material property such …

Lattice thermal conductivity (κ L) is a crucial physical property of crystals with applications in
thermal management, such as heat dissipation, insulation, and thermoelectric energy …

Electronic devices get smaller and smaller in every generation. In micro-/nano-electronic
devices such as high electron mobility transistors, heat dissipation has become a crucial …

Searching for technologically promising crystalline materials with desired thermal transport
properties requires an electronic level comprehension of interatomic interactions and …

Graph theory is a field within mathematics and computer science that studies the
relationships between objects represented in the form of graphs. One of the important …