Large-scale simulations with complex electron interactions remain one of the greatest
challenges for atomistic modelling. Although classical force fields often fail to describe the …

This review highlights recent advances in machine learning (ML)‐assisted design of energy
materials. Initially, ML algorithms were successfully applied to screen materials databases …

Computational material discovery is under intense study owing to its ability to explore the
vast space of chemical systems. Neural network potentials (NNPs) have been shown to be …

The MACE architecture represents the state of the art in the field of machine learning force
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …

Abstract Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the
lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their …

Abstract Machine learning potential (MLP) has been a popular topic in recent years for its
capability to replace expensive first-principles calculations in some large systems …

This Perspective explores the integration of machine learning potentials (MLPs) in the
research of heterogeneous catalysis, focusing on their role in identifying in situ active sites …

Atomistic simulation has a broad range of applications from drug design to materials
discovery. Machine learning interatomic potentials (MLIPs) have become an efficient …

Point clouds are versatile representations of 3D objects and have found widespread
application in science and engineering. Many successful deep-learning models have been …

We propose a machine-learning interatomic potential for multi-component magnetic
materials. In this potential we consider magnetic moments as degrees of freedom (features) …