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 …

High-temperature oxidation can precipitate chemical and mechanical degradations in
materials, potentially leading to catastrophic failures. Thus, understanding the mechanisms …

The inert gases Xe and Kr mainly exist in the used nuclear fuel (UNF) with the Xe/Kr ratio of
20: 80, which it is difficult to separate. In this work, based on the G-MOFs database, high …

In recent years,(big) data science has emerged as the “fourth paradigm” in physical science
research. Data-driven techniques, eg machine learning, are advantageous in dealing with …

In the present work, we used machine learning (ML) techniques to build a crystal-based
model that can predict the lattice thermal conductivity (LTC) of crystalline materials. To …

Thermoelectric (TE) materials can directly convert heat to electricity and vice versa and have
broad application potential for solid-state power generation and refrigeration. Over the past …

Traditional simulation methods have made prominent progress in aiding experiments for
understanding thermal transport properties of materials, and in predicting thermal …

Prediction models of lattice thermal conductivity (κL) have wide applications in the discovery
of thermoelectrics, thermal barrier coatings, and thermal management of semiconductors …

Lattice thermal conductivity is important for many applications, but experimental
measurements or first principles calculations including three-phonon and four-phonon …

Artificial Intelligence (AI) has advanced material research that were previously intractable,
for example, the machine learning (ML) has been able to predict some unprecedented …