Machine Learning Driven Automated Scanning Probe Microscopy for Material Discovery: Applications in Ferroelectric and Optoelectronic Materials

Scanning probe microscopy (SPM) has become a mainstay of the field of materials science. However, until now, the search for interesting functionalities in SPM experiments has been guided by auxiliary information to identify objects of interest and the exploration of physical mechanisms depends on human-based decision making, i.e., operators determine parameters for subsequent experiments according to the previous results. Meanwhile, machine learning (ML) has been applied to explore the physical mechanisms encoded in microscopy data. The combination of ML and SPM offers the opportunity of developing ML-driven automated SPM for the discovery of materials' functionality and mechanism in an automated manner. In this talk, I will discuss our development of ML-driven SPMs for learning the functionality and mechanism of ferroelectric materials and optoelectronic materials in an automated manner. We implemented three ML models in our SPM including a deep residual learning framework holistically-nested edge detection (ResHed) model, a deep kernel learning (DKL), and a hypothesis learning model. First, the ResHed model converts the stream image data into the semantically segmented objects of interest, then SPM can perform spectroscopic measurement on thus discovered objects automatically, allowing a systematic investigation of the discovered objects. Second, the DKL actively learns the relationship between structural elements in images and properties encoded in spectra during experiments. Third, the hypothesis learning method identifies the best physical models that can describe the material behaviours in an automated manner during the experiment. We implemented these approaches in SPM here, however, these approaches be adapted to apply to a broad range of physical and chemical experiments.