Fig. 3. General ML workflow for inverse design approaches.

Inverse design of polymers target algorithms which generate new, valid polymer structures with desired properties from property inputs. As seen in property prediction, data must be preprocessed & encoded prior to inverse design. Training involves the generation of a new structure through sequence perturbations or interpolations within existing latent spaces (represented here as ‘generator’). The properties of the new structure are predicted and compared to the target properties (shown as ‘property predictor’). The algorithms then iterate between these stages until structures with desired properties are achieved. While training of the ‘generator’ and ‘property predictor’ are approach-dependent, their hyperparameters may be tuned by minimizing the prediction error.