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

Atomic‐scale modeling and understanding of materials have made remarkable progress,
but they are still fundamentally limited by the large computational cost of explicit electronic …

We present an accurate machine learning (ML) model for atomistic simulations of carbon,
constructed using the Gaussian approximation potential (GAP) methodology. The potential …

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

We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid
and amorphous elemental carbon. Based on a machine learning representation of the …

Recently, many studies have been conducted on the use of glassy carbon (GC) in advanced
technological applications due to its excellent chemical, mechanical, electrical, and thermal …

We perform a comparative study of six common carbon interatomic potentials: Tersoff, REBO-
II, ReaxFF, EDIP, LCBOP-I and COMB3. To ensure fair comparison, all the potentials are …

A major obstacle for machine learning (ML) in chemical science is the lack of physically
informed feature representations that provide both accurate prediction and easy …

By combining different allotropic forms of carbon at the nanoscale it is possible to fabricate
tailor made surfaces with unique properties. These novel materials have shown high …

It is an established paradigm in the emerging fields of nanoscience, nanotechnology and
molecular engineering that a very important domain of fundamental research is associated …