Unsupervised Machine Learning Discovery of Chemical and Physical Transformation Pathways from Imaging Data

We show that unsupervised machine learning can be used to learn physical and chemical transformation pathways from the observational microscopic data, as demonstrated for atomically resolved images in Scanning Transmission Electron Microscopy (STEM) and ferroelectric domain structures in Piezoresponse Force Microscopy (PFM). To enable this analysis in STEM, we assumed the existence of atoms, a discreteness of atomic classes, and the presence of an explicit relationship between the observed STEM contrast and the presence of atomic units. In PFM, we assumed the uniquely-defined relationship between the measured signal and polarization distribution. With only these postulates, we developed a machine learning method leveraging a rotationally-invariant variational autoencoder (rVAE) that can identify the existing structural units observed within a material. The approach encodes the information contained in image sequences using a small number of latent variables, allowing the exploration of chemical and physical transformation pathways via the latent space of the system. The results suggest that the high-veracity imaging data can be used to derive fundamental physical and chemical mechanisms involved, by providing encodings of the observed structures that act as bottom-up equivalents of structural order parameters. The approach also demonstrates the potential of variational (i.e., Bayesian) methods for physical sciences and will stimulate the development of new ways to encode physical constraints in the encoder-decoder architectures, and generative physical laws, topological invariances, and causal relationships in the latent space of VAEs.

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