We introduce a compact cluster expansion method constructed over Jacobi and Legendre
polynomials to generate highly accurate and flexible machine-learning force fields. The …

Abstract Machine learning (ML) enables the development of interatomic potentials with the
accuracy of first principles methods while retaining the speed and parallel efficiency of …

The development of differentiable invariant descriptors for accurate representations of
atomic environments plays a central role in the success of interatomic potentials for …

Data-driven methods are receiving significant attention in recent years for chemical and
materials researches; however, more works should be done to leverage the new paradigm …

We introduce the proper orthogonal descriptors for efficient and accurate interatomic
potentials of multi-element chemical systems. The potential energy surface of a multi …

The development of interatomic potentials that can accurately capture a wide range of phys
ical phenomena and diverse environments is of significant interest, but it presents a …

The capacities of various interatomic potentials available for elemental germanium, with the
scope to choose the potential suitable for the modeling of germanene (2D germanium) …

Abstract Machine-learning models have rapidly become fundamental tools in the study of
materials properties. In the past few years there has been a surge of interest in the …

Magnetic materials have diverse applications across multiple sectors, ranging from magnetic
resonance imaging machines, used to detect diseases, to electric motors, sensors, and wind …