Two-dimensional (2D) nanomaterials exhibit unique properties due to their low dimensionality, which has led to great potential for applications in biopharmaceuticals, aerospace, energy storage, mobile communications and other fields. Today, 2D nanomaterials are often prepared and exfoliated by a combination of mechanical and manual methods, which makes the production of 2D nanomaterials inefficient and prevents standardized and industrialized manufacturing. Recent breakthroughs in semantic segmentation techniques based on deep learning have enabled the accurate identification and segmentation of atomic layers of 2D nanomaterials using optical microscopy. In this study, we analyzed in detail sixteen semantic segmentation models that perform well on public datasets and apply them to the layer identification and segmentation of graphene and molybdenum disulfide. Furthermore, we improved the U²-Net^† model to obtain the best overall performance, namely 2DU²-Net^†. The accuracy of the 2DU²-Net^† model was 99.03%, the kappa coefficient was 95.72%, the dice coefficient was 96.97%, and the average cross–merge ratio was 94.18%. Meanwhile, it also had good performance in terms of computation, number of parameters, inference speed and generalization ability. The results show that deep learning-based semantic segmentation methods can greatly improve efficiency and replace most manual operations, and different types of semantic segmentation methods can be adapted to different properties of 2D nanomaterials, thus promoting the research and application of 2D nanomaterials.

Nanoparticles have great potential for the application in new energy and aerospace fields. The distribution of nanoparticle sizes is a critical determinant of material properties and serves as a significant parameter in defining the characteristics of zero-dimensional nanomaterials. In this study, we proposed HRU²-Net^†, an enhancement of the U²-Net^† model, featuring multi-level semantic information fusion. This approach exhibits strong competitiveness and refined segmentation capabilities for nanoparticle segmentation. It achieves a Mean intersection over union (MIoU) of 87.31%, with an accuracy rate exceeding 97.31%, leading to a significant improvement in segmentation effectiveness and precision. The results show that the deep learning-based method significantly enhances the efficacy of nanomaterial research, which holds substantial significance for the advancement of nanomaterial science.

Magnetism is an important property of doped two-dimensional nanostructures. By introducing dopant atoms or molecules, the electronic structure and magnetic behavior of the two-dimensional nanostructures can be altered. However, the complexity of the doping process requires different strategies for the preparation and testing of various types, layers, and scales of doped two-dimensional materials using traditional techniques. This process is resource-intensive, inefficient, and can pose safety risks when dealing with chemically unstable materials. Deep learning-based methods offer an effective solution to overcome these challenges and improve production efficiency. In this study, a deep learning-based method is proposed for predicting the magnetism of doped two-dimensional nanostructures. An image dataset was constructed for deep learning using a publicly available database of doped two-dimensional nanostructures. The ResNet model was enhanced by incorporating the Swin Transformer module, resulting in the Swin–ResNet network architecture. A comparative analysis was conducted with various deep learning models, including ResNet, Res2net, ResneXt, and Swin Transformer, to evaluate the performance of the optimized model in predicting the magnetism of doped two-dimensional nanostructures. The optimized model demonstrated significant improvements in magnetism prediction, with a best accuracy of 0.9.

One-dimensional (1D) nanowires represent a critical class of nanomaterials with extensive applications in biosensing, biomedicine, bioelectronics, and energy harvesting. In materials science, accurately extracting their morphological and structural features is essential for effective image segmentation. However, 1D nanowires frequently appear in dispersed or entangled configurations, often with blurred backgrounds and indistinct boundaries, which significantly complicates the segmentation process. Traditional threshold-based methods struggle to segment these structurally complex nanowires with high precision. To address this challenge, we propose a wavelet-based Bilateral Segmentation Network named WaveBiSeNet, to which a Dual Wavelet Convolution Module (DWCM) and a Flexible Upsampling Module (FUM) are introduced to enhance feature representation and improve segmentation accuracy. In this study, we benchmarked WaveBiSeNet against ten segmentation models on a peptide nanowire image dataset. Experimental results demonstrate that WaveBiSeNet achieves, mIoU of 77.59%, an accuracy of 89.95%, an F1 score of 87.22%, and a Kappa coefficient of 74.13%, respectively. Compared to other advanced models, our proposed model achieves better segmentation performance. These findings demonstrate that WaveBiSeNet is an end-to-end deep segmentation network capable of accurately analyzing complex 1D nanowire structures.

Understanding and predicting the structure–property relationships in magnetic clusters remain challenging due to the complex interplay between geometry, electronic structure and spin configurations: density functional theory suffers from prohibitive computational cost for large-scale exploration, while existing graph neural networks struggle to balance long-range magnetic correlation capture, topological fidelity and scalability. We present MagCluster-GMAF, a physics-inspired hybrid graph architecture with magnetic cluster-aware serialization to preserve topological and electronic similarity during graph-to-sequence transformation, paired with a dual-branch design combining a Mamba-2 backbone for efficient long-range spin correlation modeling and a lightweight attention module as topological compensation. On a 2D doped magnetic material dataset, it achieved a total magnetic moment prediction MAE of 0.2683 μB, reducing the prediction error by 9.5% relative to the GPS + Transformer baseline and outperforming Graph-Mamba; at the 1400-node scale, it cut the GPU memory consumption by over 70% versus standard graph transformers while exhibiting near-linear empirical scaling at the studied graph scales. SHAP-based feature attribution analysis further identifies the key physicochemical descriptors governing the predictions, providing an effective approach for nanoscale magnetic material screening and the high-throughput screening of spintronic materials.