Automated structure discovery in atomic force microscopy
Automated structure discovery in atomic force microscopy
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DOI：
10.1126/sciadv.aay6913
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发表时间：
2020-02-01
期刊：
影响因子：
13.6
通讯作者：
Foster, Adam S.
中科院分区：
文献类型：
--
作者：
Alldritt, Benjamin;Hapala, Prokop;Foster, Adam S.
具有分子功能化针尖的原子力显微镜(AFM)已经成为探测有机分子表面原子结构的主要实验技术。由于难以解释源自非平面分子的高度扭曲的原子力显微镜图像，大多数实验仅限于接近平面的芳香族分子。在这里，我们开发了一个深度学习基础设施，它将一组AFM图像与表征分子构型的唯一描述符相匹配，使我们能够直接预测分子结构。我们应用这种方法基于低温AFM测量来解析1S-樟脑在铜(111)表面的几种不同的吸附构型。这一方法将为将高分辨率原子力显微镜应用于各种系统打开大门，对这些系统来说，在单个物体/分子水平上进行常规的原子和化学结构解析将是一个重大突破。
Atomic force microscopy (AFM) with molecule-functionalized tips has emerged as the primary experimental technique for probing the atomic structure of organic molecules on surfaces. Most experiments have been limited to nearly planar aromatic molecules due to difficulties with interpretation of highly distorted AFM images originating from nonplanar molecules. Here, we develop a deep learning infrastructure that matches a set of AFM images with a unique descriptor characterizing the molecular configuration, allowing us to predict the molecular structure directly. We apply this methodology to resolve several distinct adsorption configurations of 1S-camphor on Cu(111) based on low-temperature AFM measurements. This approach will open the door to applying high-resolution AFM to a large variety of systems, for which routine atomic and chemical structural resolution on the level of individual objects/molecules would be a major breakthrough.