Reward driven workflows for unsupervised explainable analysis of phases and ferroic variants from atomically resolved imaging data
This work addresses the problem of robust phase and variant identification in materials science for researchers, but it is incremental as it builds on existing unsupervised methods with a novel optimization technique.
The study tackled the sensitivity of unsupervised ML methods to hyperparameters in analyzing atomically resolved imaging data by introducing a reward-driven approach to optimize descriptors and hyperparameters, exemplified by discovering polarization and lattice distortion in Sm doped BiFeO3 thin films, and extended this to disentangle structural factors using a variational autoencoder.
Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic variants, and other pertinent aspects of materials structure from imaging data. While unsupervised methods for clustering and classification are widely used for these tasks, their performance can be sensitive to hyperparameter selection in the analysis workflow. In this study, we explore the effects of descriptors and hyperparameters on the capability of unsupervised ML methods to distill local structural information, exemplified by discovery of polarization and lattice distortion in Sm doped BiFeO3 (BFO) thin films. We demonstrate that a reward-driven approach can be used to optimize these key hyperparameters across the full workflow, where rewards were designed to reflect domain wall continuity and straightness, ensuring that the analysis aligns with the material's physical behavior. This approach allows us to discover local descriptors that are best aligned with the specific physical behavior, providing insight into the fundamental physics of materials. We further extend the reward driven workflows to disentangle structural factors of variation via optimized variational autoencoder (VAE). Finally, the importance of well-defined rewards was explored as a quantifiable measure of success of the workflow.