Overcoming the chemical complexity bottleneck in on-the-fly machine learned molecular dynamics simulations
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909222415892480 |
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| author | Timmerman, Lucas R. Kumar, Shashikant Suryanarayana, Phanish Medford, Andrew J. |
| author_facet | Timmerman, Lucas R. Kumar, Shashikant Suryanarayana, Phanish Medford, Andrew J. |
| contents | We develop a framework for on-the-fly machine learned force field molecular dynamics simulations based on the multipole featurization scheme that overcomes the bottleneck with the number of chemical elements. Considering bulk systems with up to 6 elements, we demonstrate that the number of density functional theory calls remains approximately independent of the number of chemical elements, in contrast to the increase in the smooth overlap of atomic positions scheme. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_07961 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Overcoming the chemical complexity bottleneck in on-the-fly machine learned molecular dynamics simulations Timmerman, Lucas R. Kumar, Shashikant Suryanarayana, Phanish Medford, Andrew J. Computational Physics Materials Science Chemical Physics We develop a framework for on-the-fly machine learned force field molecular dynamics simulations based on the multipole featurization scheme that overcomes the bottleneck with the number of chemical elements. Considering bulk systems with up to 6 elements, we demonstrate that the number of density functional theory calls remains approximately independent of the number of chemical elements, in contrast to the increase in the smooth overlap of atomic positions scheme. |
| title | Overcoming the chemical complexity bottleneck in on-the-fly machine learned molecular dynamics simulations |
| topic | Computational Physics Materials Science Chemical Physics |
| url | https://arxiv.org/abs/2404.07961 |