Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows

Abstract

Here, we report a case study implementation of reinforcement learning (RL) to automate operations in the scanning transmission electron microscopy (STEM) workflow. To do so, we design a virtual, prototypical RL environment to test and develop a network to autonomously align the electron beam without prior knowledge. Using this simulator, we evaluate the impact of environment design and algorithm hyperparameters on alignment accuracy and learning convergence, showing robust convergence across a wide hyperparameter space. Additionally, we deploy a successful model on the microscope to validate the approach and demonstrate the value of designing appropriate virtual environments. Consistent with simulated results, the on-microscope RL model achieves convergence to the goal alignment after minimal training. Overall, the results highlight that by taking advantage of RL, microscope operations can be automated without the need for extensive algorithm design, taking another step towards augmenting electron microscopy with machine learning methods.

References in corpus (5)

• Strengthening in multi-principal element alloys with local-chemical-order roughened dislocation pathways
• Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations
• Review: Deep Learning in Electron Microscopy
• Physics discovery in nanoplasmonic systems via autonomous experiments in Scanning Transmission Electron Microscopy
• An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

Cited by in corpus (3)

• Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images
• Evaluating Stage Motion for Automated Electron Microscopy
• Mind the Gap: Bridging the Divide Between AI Aspirations and the Reality of Autonomous Characterization