Orb: A Fast, Scalable Neural Network Potential
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913567846957056 |
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| author | Neumann, Mark Gin, James Rhodes, Benjamin Bennett, Steven Li, Zhiyi Choubisa, Hitarth Hussey, Arthur Godwin, Jonathan |
| author_facet | Neumann, Mark Gin, James Rhodes, Benjamin Bennett, Steven Li, Zhiyi Choubisa, Hitarth Hussey, Arthur Godwin, Jonathan |
| contents | We introduce Orb, a family of universal interatomic potentials for atomistic modelling of materials. Orb models are 3-6 times faster than existing universal potentials, stable under simulation for a range of out of distribution materials and, upon release, represented a 31% reduction in error over other methods on the Matbench Discovery benchmark. We explore several aspects of foundation model development for materials, with a focus on diffusion pretraining. We evaluate Orb as a model for geometry optimization, Monte Carlo and molecular dynamics simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_22570 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Orb: A Fast, Scalable Neural Network Potential Neumann, Mark Gin, James Rhodes, Benjamin Bennett, Steven Li, Zhiyi Choubisa, Hitarth Hussey, Arthur Godwin, Jonathan Materials Science Machine Learning We introduce Orb, a family of universal interatomic potentials for atomistic modelling of materials. Orb models are 3-6 times faster than existing universal potentials, stable under simulation for a range of out of distribution materials and, upon release, represented a 31% reduction in error over other methods on the Matbench Discovery benchmark. We explore several aspects of foundation model development for materials, with a focus on diffusion pretraining. We evaluate Orb as a model for geometry optimization, Monte Carlo and molecular dynamics simulations. |
| title | Orb: A Fast, Scalable Neural Network Potential |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2410.22570 |