Building nonparametric $n$-body force fields using Gaussian process regression

Understand

Constructing a classical potential suited to simulate a given atomic system is a remarkably difficult task.

• This chapter presents a framework under which this problem can be tackled, based on the Bayesian construction of nonparametric force fields of a given order using Gaussian process (GP) priors.
• The formalism of GP regression is first reviewed, particularly in relation to its application in learning local atomic energies and forces.
• For accurate regression it is fundamental to incorporate prior knowledge into the GP kernel function.