用深度学习快速识别二维材料形态，准确率超94%
Rapid morphology characterization of two-dimensional TMDs and lateral heterostructures based on deep learning
- 基于YOLO模型实现二维材料与异质结的自动识别
- 对MoS2/MoSe2异质结识别准确率达94.67%以上
- 支持显微镜实时分析，适合材料科研人员使用
二维（2D）材料及异质结具有独特物理特性，亟需高效精准的表征方法。借助人工智能进展，我们提出一种基于深度学习的方法，用于高效表征MoS2-MoSe2横向异质结以及不同形状和厚度的MoS2纳米片。通过采用YOLO模型，该方法在识别这些材料时准确率超过94.67%。此外，我们探索了跨材料迁移学习的应用，进一步提升模型性能。该模型具备强泛化能力与抗干扰性，在多种场景下均能提供可靠结果。为便于实际应用，我们开发了一款可直接从光学显微镜图像进行实时分析的应用程序，使过程显著快于传统方法且成本更低。这一深度学习驱动的方法为二维材料的快速精准表征提供了有力工具，为材料科学研究与开发开辟新路径。
原文摘要 · Abstract (English)
Two-dimensional (2D) materials and heterostructures exhibit unique physical properties, necessitating efficient and accurate characterization methods. Leveraging advancements in artificial intelligence, we introduce a deep learning-based method for efficiently characterizing heterostructures and 2D materials, specifically MoS2-MoSe2 lateral heterostructures and MoS2 flakes with varying shapes and thicknesses. By utilizing YOLO models, we achieve an accuracy rate of over 94.67% in identifying these materials. Additionally, we explore the application of transfer learning across different materials, which further enhances model performance. This model exhibits robust generalization and anti-interference ability, ensuring reliable results in diverse scenarios. To facilitate practical use, we have developed an application that enables real-time analysis directly from optical microscope images, making the process significantly faster and more cost-effective than traditional methods. This deep learning-driven approach represents a promising tool for the rapid and accurate characterization of 2D materials, opening new avenues for research and development in material science.
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