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MPIPKS:
Dynamics on nanoscale systems
Abstract:
Enabling atomic-precision mapping and manipulation of surfaces, scanning probe microscopy requires constant human supervision to assess image quality and probe conditions. Here, the authors demonstrate DeepSPM, a machine learning approach allowing to acquire and classify data autonomously in multi-day Scanning Tunnelling Microscopy experiments.
Scanning probe microscopy (SPM) has revolutionized the fields of materials, nano-science, chemistry, and biology, by enabling mapping of surface properties and surface manipulation with atomic precision. However, these achievements require constant human supervision; fully automated SPM has not been accomplished yet. Here we demonstrate an artificial intelligence framework based on machine learning for autonomous SPM operation (DeepSPM). DeepSPM includes an algorithmic search of good sample regions, a convolutional neural network to assess the quality of acquired images, and a deep reinforcement learning agent to reliably condition the state of the probe. DeepSPM is able to acquire and classify data continuously in multi-day scanning tunneling microscopy experiments, managing the probe quality in response to varying experimental conditions. Our approach paves the way for advanced methods hardly feasible by human operation (e.g., large dataset acquisition and SPM-based nanolithography). DeepSPM can be generalized to most SPM techniques, with the source code publicly available.
