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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.11773 |
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| _version_ | 1866910125899382784 |
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| author | Oppliger, J. Stifter, M. Rüegg, A. Biało, I. Martinelli, L. Freeman, P. G. Prabhakaran, D. Zhao, J. Wang, Q. Chang, J. |
| author_facet | Oppliger, J. Stifter, M. Rüegg, A. Biało, I. Martinelli, L. Freeman, P. G. Prabhakaran, D. Zhao, J. Wang, Q. Chang, J. |
| contents | Automation underpins progress across scientific and industrial disciplines. Yet, automating tasks requiring interpretation of abstract visual information remain challenging. For example, crystal alignment strongly relies on humans with the ability to comprehend diffraction patterns. Here we introduce an autonomous system that aligns single crystals without access to crystallography and diffraction theory. Using a model-free reinforcement learning framework, an agent learns to identify and navigate towards high-symmetry orientations directly from Laue diffraction patterns. Despite the absence of human supervision, the agent develops human-like strategies to achieve time-efficient alignment across different crystal symmetry classes. With this, we provide a computational framework for intelligent diffractometers. As such, our approach advances the development of automated experimental workflows in materials science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11773 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Autonomous Diffractometry Enabled by Visual Reinforcement Learning Oppliger, J. Stifter, M. Rüegg, A. Biało, I. Martinelli, L. Freeman, P. G. Prabhakaran, D. Zhao, J. Wang, Q. Chang, J. Machine Learning Materials Science Computer Vision and Pattern Recognition Automation underpins progress across scientific and industrial disciplines. Yet, automating tasks requiring interpretation of abstract visual information remain challenging. For example, crystal alignment strongly relies on humans with the ability to comprehend diffraction patterns. Here we introduce an autonomous system that aligns single crystals without access to crystallography and diffraction theory. Using a model-free reinforcement learning framework, an agent learns to identify and navigate towards high-symmetry orientations directly from Laue diffraction patterns. Despite the absence of human supervision, the agent develops human-like strategies to achieve time-efficient alignment across different crystal symmetry classes. With this, we provide a computational framework for intelligent diffractometers. As such, our approach advances the development of automated experimental workflows in materials science. |
| title | Autonomous Diffractometry Enabled by Visual Reinforcement Learning |
| topic | Machine Learning Materials Science Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.11773 |