Predicting Nanoparticle Dispersion State in Polymer Films via Machine Learning

The addition of nanoparticles into polymers can change the properties of the films. An important parameter in these nanocomposites is the aggregation state of the nanoparticles within the polymer matrix. Due to a large parameter space which may have an impact on the nanocomposite’s aggregation state, it is difficult to develop a physics based predictive model. In order to pin down which physical parameters are most important in predicting the nanoparticle dispersion in polymer films, we construct a machine learning model to predict the dispersion state of polymer nanocomposites which are produced via drop casting. Using this machine learning model, we can determine how much each individual parameter contributes to the prediction of the final aggregation state, and begin to design a physics based model using the most impactful parameters.