Shotgun crystal structure prediction using machine-learned formation energies
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866929464396480512 |
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| author | Liu, Chang Tamaki, Hiromasa Yokoyama, Tomoyasu Wakasugi, Kensuke Yotsuhashi, Satoshi Kusaba, Minoru Oganov, Artem R. Yoshida, Ryo |
| author_facet | Liu, Chang Tamaki, Hiromasa Yokoyama, Tomoyasu Wakasugi, Kensuke Yotsuhashi, Satoshi Kusaba, Minoru Oganov, Artem R. Yoshida, Ryo |
| contents | Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations. Generally, this requires repeated first-principles energy calculations, which is often impractical for large crystalline systems. Here, we present significant progress toward solving the crystal structure prediction problem: we performed noniterative, single-shot screening using a large library of virtually created crystal structures with a machine-learning energy predictor. This shotgun method (ShotgunCSP) has two key technical components: transfer learning for accurate energy prediction of pre-relaxed crystalline states, and two generative models based on element substitution and symmetry-restricted structure generation to produce promising and diverse crystal structures. First-principles calculations were performed only to generate the training samples and to refine a few selected pre-relaxed crystal structures. The ShotunCSP method is computationally less intensive than conventional methods and exhibits exceptional prediction accuracy, reaching 93.3% in benchmark tests with 90 different crystal structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_02158 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Shotgun crystal structure prediction using machine-learned formation energies Liu, Chang Tamaki, Hiromasa Yokoyama, Tomoyasu Wakasugi, Kensuke Yotsuhashi, Satoshi Kusaba, Minoru Oganov, Artem R. Yoshida, Ryo Computational Physics Materials Science Machine Learning Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations. Generally, this requires repeated first-principles energy calculations, which is often impractical for large crystalline systems. Here, we present significant progress toward solving the crystal structure prediction problem: we performed noniterative, single-shot screening using a large library of virtually created crystal structures with a machine-learning energy predictor. This shotgun method (ShotgunCSP) has two key technical components: transfer learning for accurate energy prediction of pre-relaxed crystalline states, and two generative models based on element substitution and symmetry-restricted structure generation to produce promising and diverse crystal structures. First-principles calculations were performed only to generate the training samples and to refine a few selected pre-relaxed crystal structures. The ShotunCSP method is computationally less intensive than conventional methods and exhibits exceptional prediction accuracy, reaching 93.3% in benchmark tests with 90 different crystal structures. |
| title | Shotgun crystal structure prediction using machine-learned formation energies |
| topic | Computational Physics Materials Science Machine Learning |
| url | https://arxiv.org/abs/2305.02158 |