用视觉强化学习让晶体自动对准，无需人工经验。
Autonomous Diffractometry Enabled by Visual Reinforcement Learning
- 不依赖晶体学理论，直接从劳埃衍射图学习对齐策略。
- 自主实现不同对称性晶体的高效对齐，接近人类水平。
- 适合材料科学自动化实验流程研究者参考。
自动化推动了科学与工业的进步，但涉及抽象视觉信息解读的任务仍难自动化。例如，晶体对准高度依赖人眼识别衍射图案的能力。本文提出一种无需晶体学和衍射理论知识的自主系统，基于无模型强化学习框架，代理直接从劳埃衍射图中学习识别并导航至高对称性取向。尽管无人类监督，该代理仍发展出类人策略，在多种晶体对称性下实现高效对齐。本研究为智能衍射仪提供了计算框架，推进了材料科学中自动化实验流程的发展。
原文摘要 · Abstract (English)
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.
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