The Gaussian approximation potential (GAP) is a machine learning approach to interpolating interatomic potential energy surfaces, based on Gaussian process regression. GAP is used in computational modeling of materials and molecules. GAP is also the name of the code used to perform these calculations.
On this website you can find current information and news about the code and methodology, as well as the GAP Developers & Users meetings organized by the GAP developers and its community.
The official repository for the GAP code is on Github:
For a brief introduction to the GAP methodology and an overview of the most relevant literature, check the What’s GAP page.