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.

翻译：自动化为科学和工业领域的进步奠定了基础。然而，在需要解读抽象视觉信息的任务自动化方面仍面临挑战。例如，晶体对准高度依赖人类对衍射图谱的理解能力。本文提出一种无需晶体学与衍射理论知识的自主单晶对准系统。该系统采用无模型强化学习框架，使智能体能够直接从劳厄衍射图谱中学习识别并导航至高对称取向。尽管缺乏人类监督，该智能体仍能发展出类似人类的策略，针对不同晶体对称类别实现高效对准。由此，我们为智能衍射仪提供了计算框架，推动了材料科学中自动化实验工作流程的发展。