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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.06354 |
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| _version_ | 1866910641304895488 |
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| author | Bernstein, Noam |
| author_facet | Bernstein, Noam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_06354 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From GAP to ACE to MACE Bernstein, Noam Materials Science 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. |
| title | From GAP to ACE to MACE |
| topic | Materials Science |
| url | https://arxiv.org/abs/2410.06354 |