Self-consistent Coulomb interactions for machine learning interatomic potentials
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910489093603328 |
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| author | Thomas, Jack Baldwin, William J. Csányi, Gábor Ortner, Christoph |
| author_facet | Thomas, Jack Baldwin, William J. Csányi, Gábor Ortner, Christoph |
| contents | A ubiquitous approach to obtain transferable machine learning-based models of potential energy surfaces for atomistic systems is to decompose the total energy into a sum of local atom-centred contributions. However, in many systems non-negligible long-range electrostatic effects must be taken into account as well. We introduce a general mathematical framework to study how such long-range effects can be included in a way that (i) allows charge equilibration and (ii) retains the locality of the learnable atom-centred contributions to ensure transferability. Our results give partial explanations for the success of existing machine learned potentials that include equilibriation and provide perspectives how to design such schemes in a systematic way. To complement the rigorous theoretical results, we describe a practical scheme for fitting the energy and electron density of water clusters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10915 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-consistent Coulomb interactions for machine learning interatomic potentials Thomas, Jack Baldwin, William J. Csányi, Gábor Ortner, Christoph Computational Physics Materials Science 65E05, 74E15, 81V45, 81V70 A ubiquitous approach to obtain transferable machine learning-based models of potential energy surfaces for atomistic systems is to decompose the total energy into a sum of local atom-centred contributions. However, in many systems non-negligible long-range electrostatic effects must be taken into account as well. We introduce a general mathematical framework to study how such long-range effects can be included in a way that (i) allows charge equilibration and (ii) retains the locality of the learnable atom-centred contributions to ensure transferability. Our results give partial explanations for the success of existing machine learned potentials that include equilibriation and provide perspectives how to design such schemes in a systematic way. To complement the rigorous theoretical results, we describe a practical scheme for fitting the energy and electron density of water clusters. |
| title | Self-consistent Coulomb interactions for machine learning interatomic potentials |
| topic | Computational Physics Materials Science 65E05, 74E15, 81V45, 81V70 |
| url | https://arxiv.org/abs/2406.10915 |