%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 %X
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