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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.10281 |
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| _version_ | 1866913792639631360 |
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| author | Yang, Jingyun Yin, Ruoyan Avery Jiang, Chi Hu, Yuepeng Zhu, Xiaokai Hu, Xingjian Kumar, Sutharsika Wang, Xiao Zhai, Xiaohua Rong, Keran Zhu, Yunyue Zhang, Tianyi Yin, Zongyou Kong, Jing Gong, Neil Zhenqiang Ren, Zhichu Wang, Haozhe |
| author_facet | Yang, Jingyun Yin, Ruoyan Avery Jiang, Chi Hu, Yuepeng Zhu, Xiaokai Hu, Xingjian Kumar, Sutharsika Wang, Xiao Zhai, Xiaohua Rong, Keran Zhu, Yunyue Zhang, Tianyi Yin, Zongyou Kong, Jing Gong, Neil Zhenqiang Ren, Zhichu Wang, Haozhe |
| contents | Characterization of atomic-scale materials traditionally requires human experts with months to years of specialized training. Even for trained human operators, accurate and reliable characterization remains challenging when examining newly discovered materials such as two-dimensional (2D) structures. This bottleneck drives demand for fully autonomous experimentation systems capable of comprehending research objectives without requiring large training datasets. In this work, we present ATOMIC (Autonomous Technology for Optical Microscopy & Intelligent Characterization), an end-to-end framework that integrates foundation models to enable fully autonomous, zero-shot characterization of 2D materials. Our system integrates the vision foundation model (i.e., Segment Anything Model), large language models (i.e., ChatGPT), unsupervised clustering, and topological analysis to automate microscope control, sample scanning, image segmentation, and intelligent analysis through prompt engineering, eliminating the need for additional training. When analyzing typical MoS2 samples, our approach achieves 99.7% segmentation accuracy for single layer identification, which is equivalent to that of human experts. In addition, the integrated model is able to detect grain boundary slits that are challenging to identify with human eyes. Furthermore, the system retains robust accuracy despite variable conditions including defocus, color temperature fluctuations, and exposure variations. It is applicable to a broad spectrum of common 2D materials-including graphene, MoS2, WSe2, SnSe-regardless of whether they were fabricated via chemical vapor deposition or mechanical exfoliation. This work represents the implementation of foundation models to achieve autonomous analysis, establishing a scalable and data-efficient characterization paradigm that fundamentally transforms the approach to nanoscale materials research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10281 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Zero-shot Autonomous Microscopy for Scalable and Intelligent Characterization of 2D Materials Yang, Jingyun Yin, Ruoyan Avery Jiang, Chi Hu, Yuepeng Zhu, Xiaokai Hu, Xingjian Kumar, Sutharsika Wang, Xiao Zhai, Xiaohua Rong, Keran Zhu, Yunyue Zhang, Tianyi Yin, Zongyou Kong, Jing Gong, Neil Zhenqiang Ren, Zhichu Wang, Haozhe Materials Science Mesoscale and Nanoscale Physics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Characterization of atomic-scale materials traditionally requires human experts with months to years of specialized training. Even for trained human operators, accurate and reliable characterization remains challenging when examining newly discovered materials such as two-dimensional (2D) structures. This bottleneck drives demand for fully autonomous experimentation systems capable of comprehending research objectives without requiring large training datasets. In this work, we present ATOMIC (Autonomous Technology for Optical Microscopy & Intelligent Characterization), an end-to-end framework that integrates foundation models to enable fully autonomous, zero-shot characterization of 2D materials. Our system integrates the vision foundation model (i.e., Segment Anything Model), large language models (i.e., ChatGPT), unsupervised clustering, and topological analysis to automate microscope control, sample scanning, image segmentation, and intelligent analysis through prompt engineering, eliminating the need for additional training. When analyzing typical MoS2 samples, our approach achieves 99.7% segmentation accuracy for single layer identification, which is equivalent to that of human experts. In addition, the integrated model is able to detect grain boundary slits that are challenging to identify with human eyes. Furthermore, the system retains robust accuracy despite variable conditions including defocus, color temperature fluctuations, and exposure variations. It is applicable to a broad spectrum of common 2D materials-including graphene, MoS2, WSe2, SnSe-regardless of whether they were fabricated via chemical vapor deposition or mechanical exfoliation. This work represents the implementation of foundation models to achieve autonomous analysis, establishing a scalable and data-efficient characterization paradigm that fundamentally transforms the approach to nanoscale materials research. |
| title | Zero-shot Autonomous Microscopy for Scalable and Intelligent Characterization of 2D Materials |
| topic | Materials Science Mesoscale and Nanoscale Physics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2504.10281 |