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The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements toward operationalization of the automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here, we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer, which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning. 

Selected area deposition of high purity gold films onto nanoscale 3D architectures is highly desirable as gold is conductive, inert, plasmonically active, and can be functionalized with thiol chemistries, which are useful in many biological applications. Here, we show that high-purity gold coatings can be selectively grown with the Me2Au (acac) precursor onto nanoscale 3D architectures via a pulsed laser pyrolytic chemical vapor deposition process. The selected area of deposition is achieved due to the high thermal resistance of the nanoscale geometries. Focused electron beam induced deposits (FEBID) and carbon nanofibers are functionalized with gold coatings, and we demonstrate the effects that laser irradiance, pulse width, and precursor pressure have on the growth rate. Furthermore, we demonstrate selected area deposition with a feature-targeting resolutions of ~100 and 5 µm, using diode lasers coupled to a multimode (915 nm) and single mode (785 nm) fiber optic, respectively. The experimental results are rationalized via finite element thermal modeling.