从摩尔系统STM图像利用图像识别预测原子 Hubbard U 交互
Prediction of the Hubbard U parameter from scanning tunneling microscopy images of moire systems using image recognition
AI总结：
通过图像识别从摩尔系统STM图像中预测原子Hubbard U交互，利用机器学习在扭双层石墨烯中实现高精度参数提取。
摘要：
原子Hubbard相互作用U，代表局域库仑排斥，是描述关联系统的理论模型中的关键参数，然而其在摩尔系统中的精确实验确定仍然具有挑战性。扫描隧道显微镜（STM）提供了局部态密度（LDOS）的空间图像，提供了反映材料独特电子结构的丰富数据集。在此，我们介绍了一种系统的方法，通过在扭双层石墨烯的平坦带 regime 中应用机器学习（ML），直接从这些LDOS图像中提取Hubbard U参数。即使图像相似度大于99.98%，U的回归也具有高度准确性。后续的数据分析进一步表明，在弱耦合和强耦合区域之间存在弱的交叉过渡，在Uc/t 1处。
英文摘要：
The atomistic Hubbard interaction U, representing the on-site Coulomb repulsion, serves as a pivotal parameter in theoretical models describing correlated systems, yet its precise experimental determination, especially in moire systems, remains challenging. Scanning Tunneling Microscopy (STM) provides real-space images of the local density of states (LDOS), offering rich data sets that reflect the unique electronic structure of the material. Here, we introduce a systematic methodology for extracting the Hubbard U parameter directly from these LDOS images through the application of machine learning (ML) in the case of twisted bilayer graphene in the flat-band regime. Accurate regression of U is achieved despite the extremely high visual similarity between FT-LDOS images corresponding to different interaction strengths. Subsequent data analysis further suggests a gradual interaction-dependent redistribution of spectral weight, with a possible crossover scale of order U/t ~ 1. To assess robustness beyond idealized simulations, we introduce physically motivated perturbations that emulate experimental STM imperfections and demonstrate that augmentation-based training substantially improves generalization to noisy and symmetry-broken FT-LDOS images.
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