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从扫描道显微镜图像通过人工智能方法重建原始分子轨道

Yu Zhu¹, Renjie Xue², Hao Ren³

¹Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai 200433, P. R. China.

|August 1, 2025

概括

本研究介绍了STM-Net,这是一种人工智能工具,可以从扫描道显微镜 (STM) 图像中重建分子轨道 (MO). 它克服了尖端干扰,为各种分子和基质条件揭示了高分辨率的MO特征.

科学领域:

物理化学物理化学
表面科学是一门学科.
计算化学计算化学

背景情况:

分子轨道 (MO) 是化学的基础,扫描道显微镜 (STM) 为它们的空间表征提供了潜力.
使用STM对MOs进行高分辨率成像是具有挑战性的,原因是功能化尖端的干扰,特别是高角度动量贡献.

研究的目的:

从STM图像中开发一种用于精确,高分辨率重建分子轨道的新方法.
克服MO的STM成像中的功能化提示所造成的局限性.

主要方法:

建立一个以物理为导向的深度学习网络,STM-Net,用于MO重建.
利用人工智能进行图像识别以及不同角度动量贡献的可分离特征.
网络应用于实验性STM数据,具有功能化的提示.

主要成果:

从高分辨率的STM图像中,STM-Net成功地重建了原始的分子轨道特征.
该方法证明了对各种实验观测和分子条件的直接适用性.
该框架显示了适应各种功能化的尖端状态和基板的适应性.

结论:

STM-Net提供了一个强大的解决方案,用于精确,高分辨率的分子轨道的特征.
这种以物理为导向的AI方法扩大了STM对基本化学见解的适用性.
这些发现可能会带来新的应用和对分子轨道的更深入的理解.

关键词:

在CO-functionalized提示提示.在STM-Net中,我们可以使用STM-Net.人工智能的人工智能分子轨道是分子轨道.扫描道显微镜扫描道显微镜

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