Droplet generators are at the heart of many microfluidic devices developed for life science applications but are difficult to tailor to each specific application. The high fabrication costs, complex fluid dynamics, and incomplete understanding of multi-phase flows make engineering droplet-based platforms an iterative and resource-intensive process. Large-scale experimental datasets and machine learning algorithms are employed to enable performance prediction and design automation of flow-focusing devices with high accuracy. The developed capabilities are captured in a software tool that converts high-level performance specifications to a device that delivers the desired droplet diameter and generation rate. This tool effectively eliminates the need for resource-intensive design iterations to achieve functional droplet generators.
1. Establish machine learning enables accurate performance prediction of flow-focusing droplet generators
2. Establish machine learning enables accurate design automation of flow-focusing droplet generators
3. Introduce an open-source online software for flow-focusing droplet generators, called DAFD