The first step in applying ML models to evaluate physical properties is choosing an appropriate mathematical form to be used as input. This is commonly known as featurization (or fingerprinting in the chemo-informatics literature) and it is of critical importance to the quality and interpretability of the ML models. We start with generating the simplified molecular-input line-entry system (SMILES) representations of our polymers based on their repeating units. We cap the two ends of the monomer structure with hydrogen atoms to create a consistent data set. Based on our SMILES strings, we use two common fingerprints in the literature, the Extended Connectivity Fingerprint with bond diameter four Angstroms (ECFP4) and the Molecular ACCess System (MACCS) fingerprint. MACCS is a common substructure keys-based fingerprint consisting of a binary vector of 166 bits depending on the presence of certain substructures or features from a given list of structural keys . ECFP4 is an example of a topological fingerprint that is based on analyzing all the fragments of the molecule by looking at the environment of each atom up to a set radius, and then hashing every one of these environments to create the fingerprint. One needs to be careful when using hashed fingerprints because a bit cannot be traced back to a given feature, and this may result in a given bit corresponding to more than one different feature, which is called "bit collision" . We use ECFP4, based on the Morgan algorithm , which is a 2048 bit fingerprint as implemented in RDKit. Figure shows the comparison for predicting CO 2 permeability with the random forest regression model using both fingerprints. We fit and plot the logarithmic permeability values to better visualize the data set. Both fingerprints result in R 2 value of 0.982 for the training set. However, R 2 for the test set is considerably higher when we use ECFP4 as shown in Figure .