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

The focused ion beam (FIB) is a powerful tool for fabrication, modification, and
characterization of materials down to the nanoscale. Starting with the gallium FIB, which was …

This paper summarizes the progress of machine‐learning‐based interatomic potentials and
their applications in advanced manufacturing. Interatomic potential is essential for classical …

Amorphous carbon (aC) materials have diverse interesting and useful properties, but the
understanding of their atomic-scale structures is still incomplete. Here, we report on …

Since a few breakthroughs in the fundamental understanding of the effects of swift heavy
ions (SHIs) decelerating in the electronic stopping regime in the matter have been achieved …

The mechanical performance—including deformation, fracture and radiation damage—of
zirconium is determined at the atomic scale. With Zr and its alloys extensively used in the …

Machine learning has had an enormous impact in many scientific disciplines. It has also
attracted significant interest in the field of low-temperature plasma (LTP) modeling and …

Harsh radiation environments cause displacement damages in semiconductor components,
resulting in performance degradation. Molecular simulations provide a unique approach to …

Abstract Machine learning emerges to accelerate first-principles calculations in materials
discovery and property prediction, but developing high-accuracy prediction models requires …

Characterization of the primary damage is the starting point in describing and predicting the
irradiation-induced damage in materials. So far, primary damage has been described by …