mlsanityData to 14 Oct 2026

Autonomous Diffractometry Enabled by Visual Reinforcement Learning

arXiv 2604.11773 ↗First posted 13 Apr 2026cs.LG cond-mat.mtrl-sci cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 49 references that are in the map. First posted on arXiv 13 Apr 2026.

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16 of 49 references.

References · 16

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
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  5. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
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  12. 2019Improving Sample Efficiency in Model-Free Reinforcement Learning from Imagescited by 274
4 more
  1. 2023The Dormant Neuron Phenomenon in Deep Reinforcement Learningcited by 174
  2. 2020Better Aggregation in Test-Time Augmentationcited by 122
  3. 2023DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimizationcited by 47
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  1. 2026Symmetry- and Property-Aware Crystal Generation with Reinforcement Learning for Inverse Materials Design
  2. 2026A self-learning scientific agent for X-ray diffraction
  3. 2026Autonomous Materials Exploration by Integrating Automated Phase Identification and AI-Assisted Human Reasoning
  4. 2026Reinforcement Learning on the Discrete Composition Channel of a Crystal Generator: Validated Gains and Reward Hacking
  5. 2026Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
  6. 2026Optimizing Expert-Designed Crystal Graph Networks for Band-Gap Prediction with an Autonomous LLM Research Loop
  7. 2026AutoXRD: Autonomous LLM Agents and Comprehensive Evaluation for Powder Diffraction Analysis
  8. 2026Composable Crystals: Controllable Materials Discovery via Concept Learning