使用生成模型自动解析范德华界面处的原子结构
Auto-resolving the atomic structure at van der Waals interfaces using a generative model
AI总结：
针对原子分辨率显微镜复杂图案自动解析难题，提出Trident策略增强的解耦表示学习生成模型，结合少量实验图与大量模拟图生成训练数据，训练残差网络实现范德华界面原子结构皮米级精准推断，兼具鲁棒性与高通量处理能力。
摘要：
原子结构的高分辨率可视化对于理解材料微观构型与宏观性能之间的关联具有重要意义。然而，目前仍难以实现一种快速、准确且鲁棒的方法来自动解析原子分辨率显微镜图像中的复杂图案。本文提出一种Trident策略增强的解耦表示学习方法（一种生成模型），该方法利用少量未标注的实验图像以及大量低成本的模拟图像，生成与实验结果高度相似的带标注模拟数据语料，构建出高质量的大规模训练数据集。随后通过残差神经网络训练出一个结构推断模型，该模型可直接推断多种材料（例如MoS₂、WS₂、ReS₂、ReSe₂和1T'-MoTe₂）不同层数的范德华界面处多样复杂堆叠模式的层间滑移与旋转，精度达皮米级，且对缺陷、成像质量和表面污染表现出鲁棒性。该框架还可识别图案转变界面、量化细微的基序变化，并区分频域中难以辨别的莫尔图案。最后，本方法的高通量处理能力为范德华外延生长模式提供了新见解，即多种热力学有利的滑移堆叠结构可以共存。
英文摘要：
The high-resolution visualization of atomic structures is significant for understanding the relationship between the microscopic configurations and macroscopic properties of materials. However, a rapid, accurate, and robust approach to automatically resolve complex patterns in atomic-resolution microscopy remains difficult to implement. Here, we present a Trident strategy-enhanced disentangled representation learning method (a generative model), which utilizes a few unlabelled experimental images with abundant low-cost simulated images to generate a large corpus of annotated simulation data that closely resembles experimental results, producing a high-quality large-volume training dataset. A structural inference model is then trained via a residual neural network which can directly deduce the interlayer slip and rotation of diversified and complicated stacking patterns at van der Waals interfaces with picometer-scale accuracy across various materials (e.g. MoS2, WS2, ReS2, ReSe2, and 1T'-MoTe2) with different layer numbers , demonstrating robustness to defects, imaging quality, and surface contaminations. The framework can also identify pattern transition interfaces, quantify subtle motif variations, and discriminate moire patterns that are difficult to distinguish in frequency domains. Finally, the high-throughput processing ability of our method provides insights into a van der Waals epitaxy mode where various thermodynamically favorable slip stackings can coexist.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
