Fig. 1: Schematic diagram of data-driven transport modeling framework.

Polymer structures and solvent mixtures are converted to simplified molecular-input line entry system (SMILES) strings and used as inputs for machine-learning algorithms designed to relate polymer-solvent structure to solvent diffusivities (D) and solubilities (S) within polymer membranes. These parameters – in addition to the various physicochemical properties of the solvents (e.g., molar volumes (\(\hat{V}\)), vapor pressures (p^sat), Hansen solubility parameters (δ)) at the desired operating conditions (e.g., pressure (P), temperature (T), composition of the feed mixture (x ^f), membrane thickness (\(\ell\))) – are then used as inputs into an N-component Maxwell-Stefan model that outputs a vector of fluxes (N) and compositions (x ^p) for each component permeating through the membrane.