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二维晶体由于具有原子级厚度特征及与体材料截然不同的特性而受到广泛关注，是当前多个领域的研究热点。机械剥离是获得高质量二维晶体的重要方法。然而机械剥离得到薄片通常混杂着不同的厚度，对其人工识别和手动分离费时费力。
来自日本理化技术研究所（RIKEN）高级情报项目中心（AIP）的斋藤雄（Yu Saito）和津田康治（Koji Tsuda）共同领导的团队，介绍了一种利用深层神经网络自动划分和识别2D晶体厚度的通用技术。他们构建了由卷积U-Net组成的框架，并采用24和30 张MoS2和石墨烯的光学显微图像训练模型，由此预测识别了图像中二维晶体的厚度。交叉验证得分和正确率检测发现，U-Net生成的数据正确率约为70-80％，与人类非专家水平相当。这意味着该神经网络能以实用精确度，在第一轮筛选过程中区分出Si/SiO2衬底上的MoS2和石墨烯的单层、双层和其他更厚的薄片，以便能在进一步的传输/光学实验之前进行挑选。该研究为探索基于AI的大规模快速制备2D材料和范德华异质结方法开辟了新的途径。
该文近期发表于npj Computational Materials 5: 124 (2019)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。
Deep-learning-based quality filtering of mechanically exfoliated 2D crystals 
Yu Saito, Kento Shin, Kei Terayama, Shaan Desai, Masaru Onga, Yuji Nakagawa, Yuki M. Itahashi, Yoshihiro Iwasa, Makoto Yamada & Koji Tsuda 
Two-dimensional (2D) crystals are attracting growing interest in various research fields such as engineering, physics, chemistry, pharmacy, and biology owing to their low dimensionality and dramatic change of properties compared to the bulk counter parts. Among the various techniques used to manufacture 2D crystals, mechanical exfoliation has been essential to practical applications and fundamental research.However, mechanically exfoliated crystals on substrates contain relatively thick flakes that must be found and removed manually, limiting high-throughput manufacturing of atomic 2D crystals and van der Waals heterostructures.Here, we present a deep-learning-based method to segment and identify the thickness of atomic layer flakes from optical microscopy images. Through carefully designing a neural network based on U-Net, we found that our neural network based on U-net trained only with the data based on realistically small number of images successfully distinguish monolayer and bilayer MoS2 and graphene with a success rate of 70–80%, which is a practical value in the first screening process for choosing monolayer and bilayer flakes of all flakes on substrates without human eye. The remarkable results highlight the possibility that a large fraction of manual laboratory work can be replaced by AI-based systems, boosting productivity.
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