Roccapriore Kevin M, Zou Qiang, Zhang Lizhi, Xue Rui, Yan Jiaqiang, Ziatdinov Maxim, Fu Mingming, Mandrus David G, Yoon Mina, Sumpter Bobby G, Gai Zheng, Kalinin Sergei V
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
Department of Physics and Astronomy, University of Tennessee, Knoxville, Tennessee 37996, United States.
ACS Nano. 2021 Jul 27;15(7):11806-11816. doi: 10.1021/acsnano.1c02902. Epub 2021 Jun 28.
The adatom arrays on surfaces offer an ideal playground to explore the mechanisms of chemical bonding via changes in the local electronic tunneling spectra. While this information is readily available in hyperspectral scanning tunneling spectroscopy data, its analysis has been considerably impeded by a lack of suitable analytical tools. Here we develop a machine learning based workflow combining supervised feature identification in the spatial domain and unsupervised clustering in the energy domain to reveal the details of structure-dependent changes of the electronic structure in adatom arrays on the CoSnS cleaved surface. This approach, in combination with first-principles calculations, provides insight for using artificial neural networks to detect adatoms and classifies each based on their local neighborhood comprised of other adatoms. These structurally classified adatoms are further spectrally deconvolved. The unexpected inhomogeneity of electronic structures among adatoms in similar configurations is unveiled using this method, suggesting there is not a single atomic species of adatoms, but rather multiple types of adatoms on the CoSnS surface. This is further supported by a slight contrast difference in the images (or slight size variation) of the topography of the adatoms.
表面上的吸附原子阵列提供了一个理想的平台，可通过局部电子隧穿光谱的变化来探索化学键合机制。虽然这些信息在高光谱扫描隧道光谱数据中很容易获得，但其分析却因缺乏合适的分析工具而受到很大阻碍。在此，我们开发了一种基于机器学习的工作流程，该流程结合了空间域中的监督特征识别和能量域中的无监督聚类，以揭示CoSnS解理表面上吸附原子阵列中电子结构的结构依赖性变化细节。这种方法与第一性原理计算相结合，为使用人工神经网络检测吸附原子并根据其由其他吸附原子组成的局部邻域对每个吸附原子进行分类提供了见解。这些按结构分类的吸附原子进一步进行光谱解卷积。使用该方法揭示了相似构型的吸附原子之间电子结构出人意料的不均匀性，这表明CoSnS表面上不存在单一原子种类的吸附原子，而是有多种类型的吸附原子。吸附原子形貌图像中的轻微对比度差异（或轻微尺寸变化）进一步支持了这一点。
