Using Machine Learning to Predict the Glass Transition Temperature of Polyimides

To expedite the process of discovering new polyimides, an important high-temperature polymer, we apply a machine learning approach to predict their glass transition temperature (T_g), which controls their processability and possible temperature window of applications. We have collected the structure and T_g data for 225 polyimides. For each polyimide, 1342 features are generated using its composition-based SMILE notation. The 225 data points are separated into a training and a test set. A machine learning algorithm based on the LASSO regularization is applied to the training set to construct a predictive model of T_g. In this process, the training set is split further into a training and a test subset either randomly or with the training subset being statistically representative of the entire training set. The performance of the resulting predictive model of T_g is evaluated with the independent test data set that never enters the training process. The best predictive model is obtained if the training and test subsets are split statistically and the bagging approach is used to improve its stability. The model is further tested with results from molecular dynamics simulations of new polyimides yet to be synthesized and a good agreement is observed.