We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

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 …

We develop a neuroevolution-potential (NEP) framework for generating neural network-
based machine-learning potentials. They are trained using an evolutionary strategy for …

In the past two and a half decades machine learning potentials have evolved from a special
purpose solution to a broadly applicable tool for large-scale atomistic simulations. By …

Machine learning (ML) approaches enable large-scale atomistic simulations with near-
quantum-mechanical accuracy. With the growing availability of these methods, there arises …

Ga2O3 is attractive for power devices and solar-blind ultraviolet photodetectors due to its
ultra-wide bandgap, large breakdown field, and favorable stability. However, it is difficult to …

Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for
studying molecular mechanisms in the condensed phase, however, they are too expensive …

Thermal transport properties of amorphous materials are crucial for their emerging
applications in energy and electronic devices. However, understanding and controlling …

The heat dissipation issue has now become one of the most important bottlenecks for power
electronics due to the rapid increase in power density and working frequency. Towards the …

Rapid advancements in nanotechnologies for energy conversion and transport applications
urgently require a further understanding of interfacial thermal transport and enhancement of …