Scientific exploration with expert knowledge (SEEK) in autonomous scanning probe microscopy with active learning

Abstract

Microscopy plays a foundational role in materials science, biology, and nanotechnology, offering high-resolution imaging and detailed insights into properties at the nanoscale and atomic level. Microscopy automation via active machine learning approaches is a transformative advancement, offering increased efficiency, reproducibility, and the capability to perform complex experiments. Our previous work on autonomous experimentation with scanning probe microscopy (SPM) demonstrated an active learning framework using deep kernel learning (DKL) for structure–property relationship discovery. Here we extend this approach to a multi-stage decision process to incorporate prior knowledge and human interest into DKL-based workflows, we operationalize these workflows in SPM. By integrating expected rewards from structure libraries or spectroscopic features, we enhanced the exploration efficiency of autonomous microscopy, demonstrating more efficient and targeted exploration in autonomous microscopy. These methods can be seamlessly applied to other microscopy and imaging techniques. Furthermore, the concept can be adapted for general Bayesian optimization in material discovery across a broad range of autonomous experimental fields.