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

Modern simulation techniques have reached a level of maturity which allows a wide range of
problems in chemistry and materials science to be addressed. Unfortunately, the application …

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 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 …

We present an atomic cluster expansion (ACE) for carbon that improves over available
classical and machine learning potentials. The ACE is parametrized from an exhaustive set …

Interatomic potentials approximate the potential energy of atoms as a function of their
coordinates. Their main application is the effective simulation of many-atom systems. Here …

The development of interatomic potentials employing artificial neural networks has seen
tremendous progress in recent years. While until recently the applicability of neural network …

The General Utility Lattice Program (gulp) has been extended to include the ability to
simulate polymers and surfaces, as well as adding many other new features, and the current …

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 …

We present an ab initio theoretical approach to accurately describe phonon thermal
transport in semiconductors and insulators free of adjustable parameters. This technique …