Abstract
Automated experiments in 4D scanning transmission electron microscopy (STEM) 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 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 predetermined physical phenomenon, such as strongest electric field magnitude, or in an exploratory fashion. We verify the approach first on preacquired 4D-STEM data and further implement it experimentally on an operational STEM. The experimental discovery workflow is demonstrated using graphene and subsequently extended toward a lesser-known layered 2D van der Waals material, MnPS3. This approach establishes a pathway 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.
| Original language | English |
|---|---|
| Journal | ACS Nano |
| DOIs | |
| State | Accepted/In press - 2021 |
Funding
This research is sponsored by the INTERSECT Initiative as part of the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC for the US Department of Energy under contract DE-AC05-00OR22725 (K.M.R.). This effort was based upon work supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division (M.O., O.D., S.V.K.). Work was partially supported (M.Z.) and conducted using resources supported by Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), which is a U.S. DOE Office of Science User Facility. Electron microscopy was performed using instrumentation within ORNL’s Materials Characterization Core provided by UT-Battelle, LLC, under Contract No. DE-AC05-00OR22725 with the DOE and sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy. The authors greatly appreciate the MnPS samples provided by Nan Huang and David G. Mandrus from the University of Tennessee. 3
| Funders | Funder number |
|---|---|
| CNMS | |
| Oak Ridge National Laboratory | |
| U.S. Department of Energy | DE-AC05-00OR22725 |
| Office of Science | |
| Basic Energy Sciences | |
| Oak Ridge National Laboratory | |
| Division of Materials Sciences and Engineering | |
| UT-Battelle |
Keywords
- 4D-STEM
- active learning
- automated experiment
- deep kernel learning
- graphene
- machine learning
- scanning transmission electron microscopy