Machine learning enables design automation of microfluidic flow-focusing droplet generation

Droplet-based microfluidic devices promise low-cost, high-throughput platforms for life science applications, yet adoption is hindered by the lack of predictive understanding of droplet generation, costly fabrication, and unreliable simulations. The authors present DAFD, a web-based tool that uses machine learning to predict and automate the design of flow-focusing droplet generators, achieving mean absolute errors under 10 µm for droplet diameter and 20 Hz for generation rate. The tool delivers user-specified performance within 4.2% (diameter) and 11.5% (rate) and is designed to be extensible via community-contributed data without requiring extensive ML expertise or large datasets, thereby reducing design iterations and facilitating broader use of microfluidics in the life sciences.