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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.12650 |
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| _version_ | 1866910951866892288 |
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| author | Yang, Yaotian Tang, Yiwen Chen, Yizhe Chen, Xiao Qiu, Jiangjie Xiong, Hao Yin, Haoyu Luo, Zhiyao Zhang, Yifei Tao, Sijia Li, Wentao Zhang, Qinghua Li, Yuqiang Ouyang, Wanli Zhao, Bin Wang, Xiaonan Wei, Fei |
| author_facet | Yang, Yaotian Tang, Yiwen Chen, Yizhe Chen, Xiao Qiu, Jiangjie Xiong, Hao Yin, Haoyu Luo, Zhiyao Zhang, Yifei Tao, Sijia Li, Wentao Zhang, Qinghua Li, Yuqiang Ouyang, Wanli Zhao, Bin Wang, Xiaonan Wei, Fei |
| contents | Machine learning-based interatomic potentials and force fields depend critically on accurate atomic structures, yet such data are scarce due to the limited availability of experimentally resolved crystals. Although atomic-resolution electron microscopy offers a potential source of structural data, converting these images into simulation-ready formats remains labor-intensive and error-prone, creating a bottleneck for model training and validation. We introduce AutoMat, an end-to-end, agent-assisted pipeline that automatically transforms scanning transmission electron microscopy (STEM) images into atomic crystal structures and predicts their physical properties. AutoMat combines pattern-adaptive denoising, physics-guided template retrieval, symmetry-aware atomic reconstruction, fast relaxation and property prediction via MatterSim, and coordinated orchestration across all stages. We propose the first dedicated STEM2Mat-Bench for this task and evaluate performance using lattice RMSD, formation energy MAE, and structure-matching success rate. By orchestrating external tool calls, AutoMat enables a text-only LLM to outperform vision-language models in this domain, achieving closed-loop reasoning throughout the pipeline. In large-scale experiments over 450 structure samples, AutoMat substantially outperforms existing multimodal large language models and tools. These results validate both AutoMat and STEM2Mat-Bench, marking a key step toward bridging microscopy and atomistic simulation in materials science.The code and dataset are publicly available at https://github.com/yyt-2378/AutoMat and https://huggingface.co/datasets/yaotianvector/STEM2Mat. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12650 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use Yang, Yaotian Tang, Yiwen Chen, Yizhe Chen, Xiao Qiu, Jiangjie Xiong, Hao Yin, Haoyu Luo, Zhiyao Zhang, Yifei Tao, Sijia Li, Wentao Zhang, Qinghua Li, Yuqiang Ouyang, Wanli Zhao, Bin Wang, Xiaonan Wei, Fei Computer Vision and Pattern Recognition Artificial Intelligence Machine learning-based interatomic potentials and force fields depend critically on accurate atomic structures, yet such data are scarce due to the limited availability of experimentally resolved crystals. Although atomic-resolution electron microscopy offers a potential source of structural data, converting these images into simulation-ready formats remains labor-intensive and error-prone, creating a bottleneck for model training and validation. We introduce AutoMat, an end-to-end, agent-assisted pipeline that automatically transforms scanning transmission electron microscopy (STEM) images into atomic crystal structures and predicts their physical properties. AutoMat combines pattern-adaptive denoising, physics-guided template retrieval, symmetry-aware atomic reconstruction, fast relaxation and property prediction via MatterSim, and coordinated orchestration across all stages. We propose the first dedicated STEM2Mat-Bench for this task and evaluate performance using lattice RMSD, formation energy MAE, and structure-matching success rate. By orchestrating external tool calls, AutoMat enables a text-only LLM to outperform vision-language models in this domain, achieving closed-loop reasoning throughout the pipeline. In large-scale experiments over 450 structure samples, AutoMat substantially outperforms existing multimodal large language models and tools. These results validate both AutoMat and STEM2Mat-Bench, marking a key step toward bridging microscopy and atomistic simulation in materials science.The code and dataset are publicly available at https://github.com/yyt-2378/AutoMat and https://huggingface.co/datasets/yaotianvector/STEM2Mat. |
| title | AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2505.12650 |