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

Droplet-based microfluidic devices hold immense potential in becoming inexpensive alternatives to existing screening platforms across life science applications, such as enzyme discovery and early cancer detection. However, the lack of a predictive understanding of droplet generation makes engineering a droplet-based platform an iterative and resource-intensive process. We present a web-based tool, DAFD, that predicts the performance and enables design automation of flow-focusing droplet generators. We capitalize on machine learning algorithms to predict the droplet diameter and rate with a mean absolute error of less than 10 mu m and 20 Hz. This tool delivers a user-specified performance within 4.2% and 11.5% of the desired diameter and rate. We demonstrate that DAFD can be extended by the community to support additional fluid combinations, without requiring extensive machine learning knowledge or large-scale data-sets. This tool will reduce the need for microfluidic expertise and design iterations and facilitate adoption of microfluidics in life sciences. Devices for droplet generation are at the heart of many microfluidic applications but difficult to tailor for specific cases. Lashkaripour et al. show how design customization can greatly be simplified by combining rapid prototyping with data-driven machine learning strategies.

Automated summary

Microfluidic devices based on droplet generation show great potential in various life science applications, but the lack of predictive understanding makes the design process iterative and resource-intensive. The DAFD tool utilizes machine learning algorithms to predict performance and automate the design of flow-focusing droplet generators, reducing the need for design iterations.