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Leveraging scanning tunneling microscopy (STM) for atomic-scale fabrication has led to many advancements such as the creation of atomic electron-spin qubit structures on surfaces. However, the time-consuming and tedious nature of this process calls for improvements, and this study explores the use of machine learning (ML) to automate certain steps, notably identifying appropriate atomic candidates for the structures. We classify titanium and iron atoms on a magnesium oxide (MgO) surface, which are prototypical on-surface spin qubit candidates, showing distinct topographic and spectroscopic features depending on the bonding sites of the MgO surface. Employing a semi-automated computer vision process, we train a convolutional neural network with STM topographic images and scanning tunneling spectroscopy (STS) curves of several hundred atoms. After training, the topography model achieves an 86% validation accuracy in classifying 200 new images, and the STS model, a 100% accuracy for a sample size of 100 atoms. This method extends its applicability to various nanoscopic measurements, including atomically resolved imaging and local probing of electronic properties, offering a promising approach for classifying atoms and molecules on surfaces. |
