Using Bayesian optimization, we further developed our platform to integrate self-optimization algorithms. Dynamic modification of the total flow rate in the system (f tot ), volume fraction (q water ) and reactant concentration (c polymer ) provides precise control over the nanoaggregation process, enabling the algorithm to target a predetermined assembly size. Thereby, the real-time analysis through online DLS offers insights into size distribution and dispersity of the obtained particles as well. For the nanoparticle formation, such self-optimization is more than a convenience, or a tool to reach an accuracy that a human operator would not be able to routinely achieve. Synthesis parameter interdependencies are highly complex, and a prediction of the outcome of a particular experiment is practically not possible. It is in situations like this where machine-learning, here in the form of Bayesian Optimisation (BO) can provide a very significant extra value by providing a foresight that any other operator would not be able to achieve. Indeed, as we will discuss below, the outcome of an experiment can seem almost random, even during the BO size targeting experiments. Figure illustrates the setup of the apparatus used with the core being the three pumps that are controlled by the computer. After initialization, in which flow conditions are selected randomly to create a starting database of conditions, the Bayesian optimization algorithm is trained on this initial dataset, and then interactively optimizes its output. With this procedure, several targets can be reached, that is screening of the entire parameter space (no automated optimization), direct size targeting, and finally the determination of Pareto fronts, which present a set of solutions for a given target size, aiding decisionmaking regarding the best operating parameters to optimize self-assembly and operational efficiency.