  Selection of the correct convergence angle is essential for achieving the
highest resolution imaging in scanning transmission electron microscopy (STEM).
Use of poor heuristics, such as Rayleigh's quarter-phase rule, to assess probe
quality and uncertainties in measurement of the aberration function result in
incorrect selection of convergence angles and lower resolution. Here, we show
that the Strehl ratio provides an accurate and efficient to calculate criteria
for evaluating probe size for STEM. A convolutional neural network trained on
the Strehl ratio is shown to outperform experienced microscopists at selecting
a convergence angle from a single electron Ronchigram using simulated datasets.
Generating tens of thousands of simulated Ronchigram examples, the network is
trained to select convergence angles yielding probes on average 85% nearer to
optimal size at millisecond speeds (0.02% human assessment time). Qualitative
assessment on experimental Ronchigrams with intentionally introduced
aberrations suggests that trends in the optimal convergence angle size are well
modeled but high accuracy requires extensive training datasets. This near
immediate assessment of Ronchigrams using the Strehl ratio and machine learning
highlights a viable path toward rapid, automated alignment of
aberration-corrected electron microscopes.
