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UniMR:用于扫描道显微镜的自动化分子识别的插入运行框架

Ziqiang Cao¹, Lingyin Zhang¹, Bingzheng Wu²

¹School of Computer Science and Technology, Soochow University, Suzhou, 215123, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|December 15, 2025

概括

一种新的无需培训的方法,UniMR,可以在不同系统和分辨率的扫描道显微镜图像中准确地识别分子. 这种通用分子识别器推进了微观表面化学研究.

科学领域:

表面科学是一门学科.
纳米技术纳米技术
计算化学计算化学

背景情况:

扫描道显微镜 (STM) 对分子表面化学至关重要.
在STM图像中识别分子是具有挑战性的,因为人类注释劳动和当前自动化方法的局限性.
现有的自动化方法往往需要广泛的培训,并且特定于特定的分子系统.

研究的目的:

开发一个无需培训的,用于STM图像的通用分子识别器 (UniMR).
创建一种广泛适用于各种分子系统的方法,并耐受低分辨率图像.
在微观表面化学中推进自动分子识别.

主要方法:

UniMR集成了计算机视觉工具与功能自适应选择模块.
图像被规范化以保持一致的亮度和方向.
高斯混合模型从像素矩阵 (结构特征) 和CLIP嵌入 (语义特征) 中动态选择最佳表示.
同位数相似性用于分子识别.

主要成果:

在五个不同的分子系统中,UniMR平均获得了F1评分>0.9.
该方法即使在低分辨率的STM图像中也显示出高精度.
与现有的自动识别方法相比,观察到显著的改进.

结论:

UniMR提供了一种多功能,无需培训的解决方案,用于STM中的分子识别.
这一框架可以显著帮助微观表面化学的研究人员.
在复杂的表面分析中,UniMR提高了STM的实用性.

关键词:

计算机视觉计算机视觉分子识别分子识别扫描道显微镜扫描道显微镜

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