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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2405.05156 |
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| _version_ | 1866910911287001088 |
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| author | Erhard, Linus C. Utt, Daniel Klomp, Arne J. Albe, Karsten |
| author_facet | Erhard, Linus C. Utt, Daniel Klomp, Arne J. Albe, Karsten |
| contents | Efficient, reliable and easy-to-use structure recognition of atomic environments is essential for the analysis of atomic scale computer simulations. In this work, we train two neuronal network (NN) architectures, namely PointNet and dynamic graph convolutional NN (DG-CNN) using different hyperparameters and training regimes to assess their performance in structure identification tasks of atomistic structure data. We show benchmarks on simple crystal structures, where we can compare against established methods. The approach is subsequently extended to structurally more complex SiO$_2$ phases. By making use of this structure recognition tool, we are able to achieve a deeper understanding of the crystallization process in amorphous SiO$_2$ under shock compression. Lastly, we show how the NN based structure identification workflows can be integrated into OVITO using its python interface. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05156 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Crystal structure identification with 3D convolutional neural networks with application to high-pressure phase transitions in SiO$_2$ Erhard, Linus C. Utt, Daniel Klomp, Arne J. Albe, Karsten Materials Science Efficient, reliable and easy-to-use structure recognition of atomic environments is essential for the analysis of atomic scale computer simulations. In this work, we train two neuronal network (NN) architectures, namely PointNet and dynamic graph convolutional NN (DG-CNN) using different hyperparameters and training regimes to assess their performance in structure identification tasks of atomistic structure data. We show benchmarks on simple crystal structures, where we can compare against established methods. The approach is subsequently extended to structurally more complex SiO$_2$ phases. By making use of this structure recognition tool, we are able to achieve a deeper understanding of the crystallization process in amorphous SiO$_2$ under shock compression. Lastly, we show how the NN based structure identification workflows can be integrated into OVITO using its python interface. |
| title | Crystal structure identification with 3D convolutional neural networks with application to high-pressure phase transitions in SiO$_2$ |
| topic | Materials Science |
| url | https://arxiv.org/abs/2405.05156 |