两阶段机器学习框架用于精确区分表面上的异构体和非常相似的分子
Zixuan Wei1, Qigang Zhong2, Jinbo Pan1
1Institute of Physics and University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China.
Journal of the American Chemical Society
|September 18, 2025
概括
使用卷积神经网络 (CNN) 的新框架ReSTOLO精确地识别了扫描道显微镜 (STM) 图像上的类似分子. 这种自动化方法提高了复杂表面反应的检测精度.
科学领域:
- 表面科学
- 纳米技术
- 计算化学
背景情况:
- 精确检测表面有机异构体和类似分子对于理解化学反应至关重要.
- 目前的方法缺乏在表面上区分密切相关的分子的效率,稳定性和自动化.
研究的目的:
- 开发一个自动化的,精确的框架来检测和识别表面上稀疏分布的多种类型的分子.
- 解决扫描道显微镜 (STM) 图像中具有相似特征的分子区分的挑战.
主要方法:
- 实现了一个名为ReSTOLO的双阶段卷积神经网络 (CNN) 框架.
- 使用YOLO v5.m进行分子定位和ResNet-101进行分类,通过盒子规范化连接.
- 在单个分子的增强STM图像数据库上训练模型.
主要成果:
- 与传统方法相比,精度,回忆和准确度大约提高了20%.
- 在分析六个具有几乎相同STM特征的分子的表面反应方面表现出有效性.
- 在具有相似配置的分子物种之间自动定位和区分的强大能力.
结论:
- 在复杂的表面反应中,ReSTOLO提供了一种高效的自动化解决方案.
- 这一框架显著推进了STM尖端操作表面化学领域.
- 这种方法有助于详细分析表面反应机制和分子性质.
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