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Owing to their tunability and versatility, the two-dimensional materials arean excellent platform to conduct a variety of experiments. However, laboriousdevice fabrication procedures remain as a major experimental challenge. Onebottleneck is searching small target crystals from a large number of exfoliatedcrystals that greatly vary in shapes and sizes. We present a method, based on acombination of deep reinforcement learning and object detection, to accuratelyand efficiently discover target crystals from a high resolution imagecontaining many microflakes. The proposed method dynamically zooms in to theregion of interest and inspects it with a fine detector. Our method can becustomized for searching various types of crystals with a modest computationpower. We show that our method outperformed a simple baseline in detectiontasks. Finally, we analyze the efficiency of the deep reinforcement learningagent in searching crystals. Codes are available at\url{https://github.com/stephandkim/detect_crystals}.

Dynamic zoom-in detection of exfoliated two-dimensional crystals using deep reinforcement learning