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

Ali Lashkaripour, Christopher Rodriguez, Noushin Mehdipour, Rizki Mardian, David McIntyre, Luis Ortiz, Joshua Campbell and Douglas Densmore ()
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Ali Lashkaripour: Boston University
Christopher Rodriguez: Massachusetts Institute of Technology
Noushin Mehdipour: Biological Design Center
Rizki Mardian: Biological Design Center
David McIntyre: Boston University
Luis Ortiz: Biological Design Center
Joshua Campbell: Boston University
Douglas Densmore: Biological Design Center

Nature Communications, 2021, vol. 12, issue 1, 1-14

Abstract: Abstract 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 μm 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.

Date: 2021
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DOI: 10.1038/s41467-020-20284-z

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