Predicting Microfluidic Droplet Diameters Using Machine Learning

We have successfully generated a graphic user interface that predicts microfluidic droplet diameters from an artificial intelligence (AI) neural network. The neural network inputs are fluid properties and geometries of 3D glass capillary devices. For single emulsions, the mean-squared error at the end of 100 epochs for training and validation converged to a of 3.99% and 2.49%, respectively. The deep machine learning model provides an alternative method of predicting droplet size without the need for rigorous theory. Moreover, the model can be altered to predict other microfluidic parameters or properties and could be extended to other fluids as well.