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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2410.17631 |
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| _version_ | 1866910662978961408 |
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| author | Luo, Zheng Feng, Ming Gao, Zijian Yu, Jinyang Hu, Liang Wang, Tao Xue, Shenao Zhou, Shen Ouyang, Fangping Feng, Dawei Xu, Kele Wang, Shanshan |
| author_facet | Luo, Zheng Feng, Ming Gao, Zijian Yu, Jinyang Hu, Liang Wang, Tao Xue, Shenao Zhou, Shen Ouyang, Fangping Feng, Dawei Xu, Kele Wang, Shanshan |
| contents | The emergence of deep learning (DL) has provided great opportunities for the high-throughput analysis of atomic-resolution micrographs. However, the DL models trained by image patches in fixed size generally lack efficiency and flexibility when processing micrographs containing diversified atomic configurations. Herein, inspired by the similarity between the atomic structures and graphs, we describe a few-shot learning framework based on an equivariant graph neural network (EGNN) to analyze a library of atomic structures (e.g., vacancies, phases, grain boundaries, doping, etc.), showing significantly promoted robustness and three orders of magnitude reduced computing parameters compared to the image-driven DL models, which is especially evident for those aggregated vacancy lines with flexible lattice distortion. Besides, the intuitiveness of graphs enables quantitative and straightforward extraction of the atomic-scale structural features in batches, thus statistically unveiling the self-assembly dynamics of vacancy lines under electron beam irradiation. A versatile model toolkit is established by integrating EGNN sub-models for single structure recognition to process images involving varied configurations in the form of a task chain, leading to the discovery of novel doping configurations with superior electrocatalytic properties for hydrogen evolution reactions. This work provides a powerful tool to explore structure diversity in a fast, accurate, and intelligent manner. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17631 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring structure diversity in atomic resolution microscopy with graph neural networks Luo, Zheng Feng, Ming Gao, Zijian Yu, Jinyang Hu, Liang Wang, Tao Xue, Shenao Zhou, Shen Ouyang, Fangping Feng, Dawei Xu, Kele Wang, Shanshan Materials Science Mesoscale and Nanoscale Physics Machine Learning The emergence of deep learning (DL) has provided great opportunities for the high-throughput analysis of atomic-resolution micrographs. However, the DL models trained by image patches in fixed size generally lack efficiency and flexibility when processing micrographs containing diversified atomic configurations. Herein, inspired by the similarity between the atomic structures and graphs, we describe a few-shot learning framework based on an equivariant graph neural network (EGNN) to analyze a library of atomic structures (e.g., vacancies, phases, grain boundaries, doping, etc.), showing significantly promoted robustness and three orders of magnitude reduced computing parameters compared to the image-driven DL models, which is especially evident for those aggregated vacancy lines with flexible lattice distortion. Besides, the intuitiveness of graphs enables quantitative and straightforward extraction of the atomic-scale structural features in batches, thus statistically unveiling the self-assembly dynamics of vacancy lines under electron beam irradiation. A versatile model toolkit is established by integrating EGNN sub-models for single structure recognition to process images involving varied configurations in the form of a task chain, leading to the discovery of novel doping configurations with superior electrocatalytic properties for hydrogen evolution reactions. This work provides a powerful tool to explore structure diversity in a fast, accurate, and intelligent manner. |
| title | Exploring structure diversity in atomic resolution microscopy with graph neural networks |
| topic | Materials Science Mesoscale and Nanoscale Physics Machine Learning |
| url | https://arxiv.org/abs/2410.17631 |