Creation of Polymer Datasets with Targeted Backbones for Screening of High-Performance Membranes for Gas Separation

A simple approach was developed to computationally construct
a
polymer dataset by combining simplified molecular-input line-entry
system (SMILES) strings of a targeted polymer backbone and a variety
of molecular fragments. This method was used to create 14 polymer
datasets by combining seven polymer backbones and molecules from two
large molecular datasets (MOSES and QM9). Polymer backbones that were
studied include four polydimethylsiloxane (PDMS) based backbones,
poly(ethylene oxide) (PEO), poly(allyl glycidyl ether) (PAGE), and
polyphosphazene (PPZ). The generated polymer datasets can be used
for various cheminformatics tasks, including high-throughput screening
for gas permeability and selectivity. This study utilized machine
learning (ML) models to screen the polymers for CO2/CH4 and CO2/N2 gas separation using membranes.
Several polymers of interest were identified. The results highlight
that employing an ML model fitted to polymer selectivities leads to
higher accuracy in predicting polymer selectivity compared to using
the ratio of predicted permeabilities.