Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows
This work addresses automation challenges for microscopy researchers, but it is incremental as it builds on existing machine learning methods for microscopy workflows.
The researchers tackled the problem of automating electron beam alignment in scanning transmission electron microscopy by implementing a reinforcement learning approach, resulting in a model that achieved convergence to goal alignment with minimal training after validation on a microscope.
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.