%ALashkaripour, Ali%ARodriguez, Christopher%AMehdipour, Noushin%AMardian, Rizki%AMcIntyre, David%AOrtiz, Luis%ACampbell, Joshua%ADensmore, Douglas%BJournal Name: Nature Communications; Journal Volume: 12; Journal Issue: 1; Related Information: CHORUS Timestamp: 2022-12-02 05:15:13 %D2021%INature Publishing Group %JJournal Name: Nature Communications; Journal Volume: 12; Journal Issue: 1; Related Information: CHORUS Timestamp: 2022-12-02 05:15:13 %K %MOSTI ID: 10208579 %PMedium: X %TMachine learning enables design automation of microfluidic flow-focusing droplet generation %XAbstract

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 10μm 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.

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