  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.
