The Gaussian approximation potential (GAP) machine-learning-inspired functional form was the first to be used for a general-purpose interatomic potential. The atomic cluster expansion (ACE), previously the subject of a KIM Review, and its multilayer neural-network extension (MACE) have joined GAP among the methods widely used for machine-learning interatomic potentials. Here I review extensions to the original GAP formalism, as well as ACE and MACE-based frameworks that maintain the good features and mitigate the limitations of the original GAP approach.
Cite
@article{arxiv.2410.06354,
title = {From GAP to ACE to MACE},
author = {Noam Bernstein},
journal= {arXiv preprint arXiv:2410.06354},
year = {2024}
}