论文标题
通过加强学习增强的工作流程增强显微镜
Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows
论文作者
论文摘要
在这里，我们报告了强化学习（RL）的案例研究实施，以自动化扫描传输电子显微镜（STEM）工作流程的操作。为此，我们设计了一个虚拟的，典型的RL环境，以测试和开发网络，以自主对电子束的自主对齐，而没有事先知识。使用此模拟器，我们评估了环境设计和算法超标仪对比对准确性和学习收敛的影响，从而显示了宽阔的超级参数空间的稳健收敛。此外，我们在显微镜上部署了成功的模型，以验证该方法并演示设计适当的虚拟环境的价值。与模拟结果一致，微观RL模型在最小训练后达到了与目标一致性的收敛。总体而言，结果表明，通过利用RL，可以自动化显微镜操作而无需进行广泛的算法设计，从而通过机器学习方法迈出了增强电子显微镜的又一步。
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.
