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Selection of the correct convergence angle is essential for achieving thehighest resolution imaging in scanning transmission electron microscopy (STEM).Use of poor heuristics, such as Rayleigh's quarter-phase rule, to assess probequality and uncertainties in measurement of the aberration function result inincorrect selection of convergence angles and lower resolution. Here, we showthat the Strehl ratio provides an accurate and efficient to calculate criteriafor evaluating probe size for STEM. A convolutional neural network trained onthe Strehl ratio is shown to outperform experienced microscopists at selectinga convergence angle from a single electron Ronchigram using simulated datasets.Generating tens of thousands of simulated Ronchigram examples, the network istrained to select convergence angles yielding probes on average 85% nearer tooptimal size at millisecond speeds (0.02% human assessment time). Qualitativeassessment on experimental Ronchigrams with intentionally introducedaberrations suggests that trends in the optimal convergence angle size are wellmodeled but high accuracy requires extensive training datasets. This nearimmediate assessment of Ronchigrams using the Strehl ratio and machine learninghighlights a viable path toward rapid, automated alignment ofaberration-corrected electron microscopes.

Optimal STEM Convergence Angle Selection using a Convolutional Neural Network and the Strehl Ratio