Automated experiment in 4D-STEM: exploring emergent physics and structural behaviors

TL;DR

This paper presents an automated 4D-STEM experimental framework utilizing deep kernel learning for active, physics-driven exploration of complex materials, enabling rapid discovery of structural and electronic properties both in simulations and real experiments.

Contribution

It introduces a novel automated, physics-guided experimental approach in 4D-STEM using deep kernel learning for active material exploration.

Findings

Successful verification on pre-acquired data

Experimental demonstration on graphene and MnPS3

Effective navigation of sample structures based on physical phenomena

Abstract

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.