Since its development in the 1970s, the rechargeable alkali-ion battery has proven to be a
truly transformative technology, providing portable energy storage for devices ranging from …

Multi-component alloys have demonstrated excellent performance in various applications,
but the vast range of possible compositions and microstructures makes it challenging to …

The subject of this paper is the technology (the'how') of constructing machine-learning
interatomic potentials, rather than science (the'what'and'why') of atomistic simulations using …

Machine learning of the quantitative relationship between local environment descriptors and
the potential energy surface of a system of atoms has emerged as a new frontier in the …

The use of supervised machine learning to develop fast and accurate interatomic potential
models is transforming molecular and materials research by greatly accelerating atomic …

The on-the-fly generation of machine-learning force fields by active-learning schemes
attracts a great deal of attention in the community of atomistic simulations. The algorithms …

Phonon anharmonicity is critical for accurately predicting the material's thermal conductivity
(κ). However, its calculation based on the perturbation theory is a difficult and time …

Lithium superionic conductors (LSCs) are of major importance as solid electrolytes for next-
generation all-solid-state lithium-ion batteries. While ab initio molecular dynamics have …

With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …

While lattice thermal conductivity is an important parameter for many technological
applications, its calculation is a time-consuming task, especially for compounds with a …