Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

A simultaneously accurate and computationally efficient parametrization of the potential
energy surface of molecules and materials is a long-standing goal in the natural sciences …

Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a
fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow …

Batteries are of paramount importance for the energy storage, consumption, and
transportation in the current and future society. Recently machine learning (ML) has …

Batteries are vital energy storage carriers in industry and in our daily life. There is continued
interest in the developments of batteries with excellent service performance and safety …

Machine-learning-based materials property prediction models have emerged as a promising
approach for new materials discovery, among which the graph neural networks (GNNs) …

Atomistic modeling of chemically reactive systems has so far relied on either expensive ab
initio methods or bond-order force fields requiring arduous parametrization. Here, we …

Over the last decade, exsolution has emerged as a powerful new method for decorating
oxide supports with uniformly dispersed nanoparticles for energy and catalytic applications …

Molecular Dynamics (MD) simulation is a powerful tool for understanding the dynamics and
structure of matter. Since the resolution of MD is atomic-scale, achieving long timescale …