  On- and off-axis electron energy loss spectroscopy (EELS) is a powerful
method for probing local electronic structure on single atom level. However,
many materials undergo electron-beam induced transformation during the scanning
transmission electron microscopy (STEM) and spectroscopy, the problem
particularly acute for off-axis EELS signals. Here, we propose and
operationalize the rapid object detection and action system (RODAS) for dynamic
exploration of the structure-property relationships in STEM-EELS. In this
approach, the electron beam is used to induce dynamic transformations creating
new defect types at sufficiently small rates and avoiding complete material
destruction. The deep convolutional neural networks trained via the ensemble
learning iterative training (ELIT) approach are used to identify the defects as
they form and perform EELS measurements only at specific defect types. Overall,
in this case the EEL spectra are collected only at predefined objects of
interest, avoiding measurements on the ideal regions or holes. We note that
this approach can be extended to identify new defect classes as they appear,
allowing for efficient collection of structure-property relationship data via
balanced sampling over defect types.
