Zero-shot Autonomous Microscopy for Scalable and Intelligent Characterization of 2D Materials
Journal:
arXiv
Published Date:
Apr 14, 2025
Abstract
Characterization of atomic-scale materials traditionally requires human
experts with months to years of specialized training. Even for trained human
operators, accurate and reliable characterization remains challenging when
examining newly discovered materials such as two-dimensional (2D) structures.
This bottleneck drives demand for fully autonomous experimentation systems
capable of comprehending research objectives without requiring large training
datasets. In this work, we present ATOMIC (Autonomous Technology for Optical
Microscopy & Intelligent Characterization), an end-to-end framework that
integrates foundation models to enable fully autonomous, zero-shot
characterization of 2D materials. Our system integrates the vision foundation
model (i.e., Segment Anything Model), large language models (i.e., ChatGPT),
unsupervised clustering, and topological analysis to automate microscope
control, sample scanning, image segmentation, and intelligent analysis through
prompt engineering, eliminating the need for additional training. When
analyzing typical MoS2 samples, our approach achieves 99.7% segmentation
accuracy for single layer identification, which is equivalent to that of human
experts. In addition, the integrated model is able to detect grain boundary
slits that are challenging to identify with human eyes. Furthermore, the system
retains robust accuracy despite variable conditions including defocus, color
temperature fluctuations, and exposure variations. It is applicable to a broad
spectrum of common 2D materials-including graphene, MoS2, WSe2, SnSe-regardless
of whether they were fabricated via chemical vapor deposition or mechanical
exfoliation. This work represents the implementation of foundation models to
achieve autonomous analysis, establishing a scalable and data-efficient
characterization paradigm that fundamentally transforms the approach to
nanoscale materials research.
