Accelerating Structure Prediction of Molecular Crystals using Actively Trained Moment Tensor Potential
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910633235054592 |
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| author | Rybin, Nikita Novikov, Ivan S. Shapeev, Alexander |
| author_facet | Rybin, Nikita Novikov, Ivan S. Shapeev, Alexander |
| contents | Inspired by the recent success of machine-learned interatomic potentials for crystal structure prediction of the inorganic crystals, we present a methodology that exploits Moment Tensor Potentials and active learning (based on maxvol algorithm) to accelerate structure prediction of molecular crystals. Benzene and glycine are used as test systems. Interestingly, among obtained low energy structures of benzene we have found a peculiar polymeric benzene structure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03484 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating Structure Prediction of Molecular Crystals using Actively Trained Moment Tensor Potential Rybin, Nikita Novikov, Ivan S. Shapeev, Alexander Materials Science Soft Condensed Matter Inspired by the recent success of machine-learned interatomic potentials for crystal structure prediction of the inorganic crystals, we present a methodology that exploits Moment Tensor Potentials and active learning (based on maxvol algorithm) to accelerate structure prediction of molecular crystals. Benzene and glycine are used as test systems. Interestingly, among obtained low energy structures of benzene we have found a peculiar polymeric benzene structure. |
| title | Accelerating Structure Prediction of Molecular Crystals using Actively Trained Moment Tensor Potential |
| topic | Materials Science Soft Condensed Matter |
| url | https://arxiv.org/abs/2410.03484 |