Abstract

Computer vision is a useful reaction monitoring and characterisation tool for scientists seeking to accelerate discovery processes using automation and machine learning (ML). Here we report a non-invasive laser-based method that combines computer vision and deep learning models to classify the solubility of different polymeric compounds across a range of solvents. Classifications were conducted using two to four solubility classes (soluble, soluble-colloidal, partially soluble, and insoluble), achieving high test accuracy rates ranging from 94.1% (2 classes), to 89.5% (4 classes). Using results from our solubility screening method, we also determined the Hansen Solubility Parameters (HSP) of the polymers using an optimisation algorithm. The calculated percentage Euclidean distance between the HSP values obtained from our dataset and the literature HSP values for the polymers, ranged from 11-32%. Finally, we developed the feature-wise linear modulation (FiLM) conditioned Convolutional Neural Network (CNN) regression model to estimate the size of polymeric nanoparticles between 20—440 nm and achieved a Mean Absolute Error (MAE) of 9.91 nm.