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

1.1. The Meaning of “Large Scale” In general, the term “large scale” can have one of the
following five meanings in computational chemistry:(1) large size: power-law and …

The success of first-principles electronic-structure calculation for predictive modeling in
chemistry, solid-state physics, and materials science is constrained by the limitations on …

The reactive force-field (ReaxFF) interatomic potential is a powerful computational tool for
exploring, developing and optimizing material properties. Methods based on the principles …

Determining the stability of molecules and condensed phases is the cornerstone of atomistic
modeling, underpinning our understanding of chemical and materials properties and …

The atomic cluster expansion is a general polynomial expansion of the atomic energy in
multi-atom basis functions. Here we implement the atomic cluster expansion in the …

To investigate the initial chemical events associated with high-temperature gas-phase
oxidation of hydrocarbons, we have expanded the ReaxFF reactive force field training set to …

Dynamic fracture is an important area of materials analysis, assessing the atomic-level
mechanisms by which materials fail over time. Here, we focus on brittle materials failure and …

Attention-based transformer neural networks have had significant impact in recent years.
However, their applicability to model the behavior of physical systems has not yet been …

We report molecular dynamics (MD) simulations using the first-principles-based ReaxFF
reactive force field to study the thermal decomposition of 1, 3, 5-triamino-2, 4, 6 …