Reinforced active learning for CVD-grown two-dimensional materials characterization

Two-dimensional (2D) materials are one of the research frontiers in material science due to their promising properties. Chemical Vapor Deposition (CVD) is the most widely used technique to grow large-scale high-quality 2D materials. The CVD-grown 2D materials can be efficiently characterized by an optical microscope. However, annotating microscopy images to distinguish the growth quality from good to bad is time-consuming. In this work, we explore Active Learning (AL), which iteratively acquires quality labels from a human and updates the classifier for microscopy images. As a result, AL only requires a limited amount of labels to achieve a good model performance. However, the existing handcrafted query strategies in AL are not good at dealing with the dynamics during the query process since the rigid handcrafted query strategies may not be able to choose the most informative instances (i.e., images) after each query. We propose a Reinforced Active Learning (RAL) framework that uses reinforcement learning to learn a query strategy for AL. Besides, by introducing the intrinsic motivation into the proposed framework, a unique intrinsic reward is designed to enhance the classification performance. The results show that RAL outperforms AL, and can significantly reduce the annotation efforts for the CVD-grown 2D materials characterization.