Confocal microscopy. Using the OT-2, 10 µL of Nile red was added to 100 µL of each sample, then 25 µL of each sample was transferred from 96 well-plate to 384 well-plate fitted with a coverslip base (Cellvis, USA) for automated confocal microscopy. Samples were imaged using a Nikon AXR confocal microscope with a 60X/1.40 Plan Apo Lambda D oil immersion lens and 561 nm excitation. A custom routine for automated 384 well-plate imaging was programmed using NIS Elements' JOBS function. Each well was imaged from 4 random fields, and exported as a TIFF file. The raw microscopy data is available at Image analysis. Depending on the image quality (determined by manual inspection), images were first preprocessed by background subtraction using a Gaussian filter, contrast enhancement using CLAHE (Contrast Limited Adaptive Histogram Equalization), and noise suppression using a Gaussian blur. Vesicle detection and counting were performed using template matching, a computer vision technique that finds the location of a small template within a larger image by sliding a template image over the target image to find high-similarity regions. More specifically, a multi-template multi-scale template matching (MMTM) algorithm was used based on previously developed for extracellular vesicle counting, as implemented in the cv2 module from the OpenCV library. Source code is available at Surrogate model and active learning. Active learning is a machine learning approach that selects the most informative data points for labeling in an iterative setting. In this work, we used active learning to efficiently design experiments, achieving desired performance with fewer iterations. This technique is particularly valuable in scenarios where experiments are labor-and time-intensive. This work used two strategies-Bayesian optimization (BO) with a Gaussian Process Regression (GPR) model and uncertainty sampling with a Gaussian Process Classifier (GPC) model. The input to both models was a seven-dimensional vector, where each dimension represents the concentration of the seven amphiphile species in units of millimolar. BO was performed using a Matern kernel to identify amphiphile compositions most likely to form vesicles, focusing on minimizing total concentration. The probability of improvement (PI) and expected improvement (EI) acquisition functions were used to select subsequent experiments. GPC was employed to solve binary classification problems, with entropy-based uncertainty used to select the next experiments. GPR and GPC models were implemented by using the botorch and scikit-learn 34 package. Program code is available at