Work on such systematic variation and on machine-learning approaches to control the particle size are underway in our laboratories at present. Automation in synthesis holds very high promise towards the future. In order to employ datadriven methods in everyday lab tasks, it is important to be able to deliver large amounts of data in short time. Thus, this study marks a profound point in that it makes such data available for the first time for block copolymer nanoparticle synthesis. In the long run, we hope this methodology will be an enabler for a much better rational design of nanoparticles in the future.