However, we observed that the turbidity was very low (< 0.05 AU at 450 nm) in many of our vesicle-containing samples, and therefore the typical single-wavelength measurement was of limited value. During the experimental process, we generated 5644 samples for which we had both UV/vis spectra and confocal microscopy quantification, which could be used as inputs for machine learning analysis. We trained three different classifier models to predict whether there was at least one vesicle observed in the (ground truth) confocal microscopy image. Figure shows the tradeoff between the optimal tradeoff between false positive and true positive rates for the different methods; a perfect method would have an area under the curve (AUC) of one, and higher values are better. The baseline model (green curve) implements the typical strategy of defining a threshold value for the absorbance at 450 nm to indicate the presence of vesicles; each point along the curve is a different choice of the threshold value, illustrating the sensitivity/selectivity tradeoff. Better performance is obtained by training a random forest classifier on the 450nm values (blue) curve, but the best performance is obtained by using the entire UV/vis spectral data as input to the random forest model (red). We suspect this is because it accounts for non-linearities in the input. (Similar results are obtained with other classifier models, such as support vector machines.) This high accuracy of the RF method (as measured against the ground truth of confocal microscopy) and its much faster and simpler operation mean that it would be well suited for serving as an initial, high-throughput screening technique for multi-fidelity optimization methods, which could be explored in future studies. More generally, this also demonstrates an additional benefit of automated experimentation-the acquisition a systematic, machine-readable dataset of results which can be data-mined after the fact for new insights. 4 Conclusion