Autonomous Diffractometry Enabled by Visual Reinforcement Learning
J. Oppliger, M. Stifter, A. Rüegg, I. Biało, L. Martinelli + 5 more
TLDR
This paper introduces an autonomous system that uses visual reinforcement learning to align single crystals from diffraction patterns, enabling intelligent diffractometers.
Key contributions
- Develops an autonomous system for single crystal alignment using visual reinforcement learning.
- Agent learns to interpret Laue diffraction patterns directly, without crystallography theory.
- Achieves human-like, time-efficient alignment across diverse crystal symmetry classes.
Why it matters
Automating tasks requiring abstract visual interpretation, like crystal alignment, is challenging. This work provides a novel, theory-free RL approach to overcome this, enabling intelligent diffractometers. It significantly advances automated experimental workflows in materials science.
Original Abstract
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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