Fig. 2: General ML workflow for property prediction tasks.

Data (i.e., polymers with known properties) must be preprocessed and encoded before passing desired input (e.g., encoded chemical structure, molecular descriptors) into a prediction algorithm. Irrespective of algorithm choice, training proceeds by tuning the model hyperparameters to minimize prediction error. The trained algorithm can then be used to screen polymer candidates prior to experimental synthesis & characterization. While deep learning and ensemble methods are the most widely used, other supervised methods have been employed (see Table 3) and may be preferred based on the application.