Automated experiments in 4D Scanning Transmission Electron Microscopy are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric and magnetic fields in complex materials. Deep kernel learning enables active learning of the relationship between local structure and a 4D-STEM based descriptors. With this, efficient and "intelligent" probing of dissimilar structural elements to discover desired physical functionality is made possible. This approach allows effective navigation of the sample in an automated fashion guided by either a pre-determined physical phenomenon, such as strongest electric field magnitude, or in an exploratory fashion. We verify the approach first on pre-acquired 4D-STEM data, and further implement it experimentally on an operational STEM. The experimental discovery workflow is demonstrated using graphene, and subsequently extended towards a lesser-known layered 2D van der Waal material, MnPS3. This approach establishes a paradigm for physics-driven automated 4D-STEM experiments that enable probing the physics of strongly correlated systems and quantum materials and devices, as well as exploration of beam sensitive materials.
@article{arxiv.2112.04479,
title = {Automated experiment in 4D-STEM: exploring emergent physics and structural behaviors},
author = {Kevin M. Roccapriore and Ondrej Dyck and Mark P. Oxley and Maxim Ziatdinov and Sergei V. Kalinin},
journal= {arXiv preprint arXiv:2112.04479},
year = {2022}
}
Comments
The data used for analysis as well as additional materials are available through the Jupyter notebook located at: https://github.com/kevinroccapriore/AE-DKL-4DSTEM