Since the isolation of graphene, two-dimensional (2D) materials have attracted increasing interest because of their excellent chemical and physical properties, as well as promising applications. Nonetheless, particular challenges persist in their further development, particularly in the effective identification of diverse 2D materials, the domains of large-scale and high-precision characterization, also intelligent function prediction and design. These issues are mainly solved by computational techniques, such as density function theory and molecular dynamic simulation, which require powerful computational resources and high time consumption. The booming deep learning methods in recent years offer innovative insights and tools to address these challenges. This review comprehensively outlines the current progress of deep learning within the realm of 2D materials. Firstly, we will briefly introduce the basic concepts of deep learning and commonly used architectures, including convolutional neural and generative adversarial networks, as well as U-net models. Then, the characterization of 2D materials by deep learning methods will be discussed, including defects and materials identification, as well as automatic thickness characterization. Thirdly, the research progress for predicting the unique properties of 2D materials, involving electronic, mechanical, and thermodynamic features, will be evaluated succinctly. Lately, the current works on the inverse design of functional 2D materials will be presented. At last, we will look forward to the application prospects and opportunities of deep learning in other aspects of 2D materials. This review may offer some guidance to boost the understanding and employing novel 2D materials.

自从石墨烯被分离出来以来，二维（2D）材料因其优异的化学和物理性能以及广阔的应用前景而引起了越来越多的关注。尽管如此，它们的进一步发展仍然存在特殊的挑战，特别是在有效识别各种二维材料、大规模和高精度表征以及智能功能预测和设计领域。这些问题主要通过密度函数理论、分子动力学模拟等计算技术来解决，需要强大的计算资源和较高的时间消耗。近年来蓬勃发展的深度学习方法为应对这些挑战提供了创新的见解和工具。这篇综述全面概述了二维材料领域深度学习的当前进展。首先，我们将简要介绍深度学习的基本概念和常用架构，包括卷积神经网络和生成对抗网络，以及 U-net 模型。然后，将讨论通过深度学习方法表征二维材料，包括缺陷和材料识别，以及自动厚度表征。第三，将简要评估预测二维材料独特性能（涉及电子、机械和热力学特征）的研究进展。最近，将介绍功能性二维材料逆向设计的最新研究成果。最后我们展望一下深度学习在二维材料其他方面的应用前景和机会。这篇综述可能会为促进理解和使用新型二维材料提供一些指导。