At the forefront of our endeavors is the integration of machine learning and AI with advanced scanning probe microscopy (SPM) techniques. We pioneer automated and intelligent SPM systems leveraging data-driven approaches to accelerate discoveries and gain insights into materials. Our automated frameworks combine autonomous SPM experiments, data visualization, and active learning algorithms. This synergistic approach enables efficient exploration of vast parameter spaces, pattern identification, and iterative refinement of our understanding of complex materials.
Machine learning models and intelligent automation allow us to navigate high-dimensional SPM data landscapes, optimizing protocols and uncovering novel phenomena. We study the impact of initial conditions and interventions on learning dynamics in autonomous workflows. Additionally, we integrate AI/ML techniques with advanced SPM modalities like ferroelectric studies, spectroscopic imaging, and multimodal characterization. Our methods extract insights from high-dimensional data, unraveling structure-property relationships and elucidating material behavior mechanisms. Through this pioneering work, we accelerate discoveries and drive transformative advances across disciplines.
Check here for more demos of our automated SPM realizations