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arXiv · 2410.06354

From GAP to ACE to MACE

Noam Bernstein
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Abstract

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.

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Noam Bernstein. 2024-10-08. From GAP to ACE to MACE. https://doi.org/10.25950/67c762ea

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