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From GAP to ACE to MACE

Noam Bernstein
Center for Materials Physics and Technology, U. S. Naval Research Laboratory, Washington, D.C.

Commentary on

A.P. Bartók, M.C. Payne, R. Kondor, and G. Csányi, “Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons”, Phys. Rev. Lett., 104:136403 (2010), https://doi.org/10.1103/PhysRevLett.104.136403
I. Batatia, D. P. Kovács, G. Simm, C. Ortner, and G. Csányi, “MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields”, Adv. Neural. Inf. Process. Syst., 35:11423 (2022), https://proceedings.neurips.cc/paper_files/paper/2022/hash/4a36c3c51af11ed9f34615b81edb5bbc-Abstract-Conference.html

Read the Commentary (PDF)

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Statement of Significance

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.

How to cite

Cite the commentary as:

N. Bernstein, "From GAP to ACE to MACE", KIM REVIEW, Volume 2, Article 05, 2024. DOI: 10.25950/67c762ea

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