1 Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montréal, Québec, H3G 1M8, Canada. steve.shih@concordia.ca.
2 PERFORM Centre, Concordia University, 7200 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada.
3 Centre for Applied Synthetic Biology, Concordia University, 7141 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada.
4 Department of Mechanical & Industrial Engineering, University of Toronto, 5 King's College Rd, Toronto, Ontario, M5S 3G8, Canada.
5 McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montréal, Québec, H3A 2B4, Canada.

Digital microfluidics (DMF) has the signatures of an ideal liquid handling platform - as shown through almost two decades of automated biological and chemical assays. However, in the current state of DMF, we are still limited by the number of parallel biological or chemical assays that can be performed on DMF. Here, we report a new approach that leverages design-of-experiment and numerical methodologies to accelerate experimental optimization on DMF. The integration of the one-factor-at-a-time (OFAT) experimental technique with machine learning algorithms provides a set of recommended optimal conditions without the need to perform a large set of experiments. We applied our approach towards optimizing the radiochemistry synthesis yield given the large number of variables that affect the yield. We believe that this work is the first to combine such techniques which can be readily applied to any other assays that contain many parameters and levels on DMF.