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Atomic-scale characterization and manipulation with scanning probe microscopy rely upon the use of an atomically sharp probe. Here we present automated methods based on machine learning to automatically detect and recondition the quality of the probe of a scanning tunneling microscope. As a model system, we employ these techniques on the technologically relevant hydrogen-terminated silicon surface, training the network to recognize abnormalities in the appearance of surface dangling bonds. Of the machine learning methods tested, a convolutional neural network yielded the greatest accuracy, achieving a positive identification of degraded tips in 97% of the test cases. By using multiple points of comparison and majority voting, the accuracy of the method is improved beyond 99%.

Supporting functional molecules on crystal facets is an established technique in nanotechnology. To preserve the original activity of ionic metallorganic agents on a supporting template, conservation of the charge and oxidation state of the active center is indispensable. We present a model system of a metallorganic agent that, indeed, fulfills this design criterion on a technologically relevant metal support with potential impact on Au(III)-porphyrin-functionalized nanoparticles for an improved anticancer-drug delivery. Employing scanning tunneling microscopy and -spectroscopy in combination with photoemission spectroscopy, we clarify at the single-molecule level the underlying mechanisms of this exceptional adsorption mode. It is based on the balance between a high-energy oxidation state and an electrostatic screening-response of the surface (image charge). Modeling with first principles methods reveals submolecular details of the metal-ligand bonding interaction and completes the study by providing an illustrative electrostatic model relevant for ionic metalorganic agent molecules, in general.