Computational Characterization of Ultrathin Polymer Membranes in Liquids

Molecular Dynamics Simulation Study of Organic Solvents Confined in PIM-1 and P84 Polyimide Membranes

Organic solvent nanofiltration (OSN) has recently proved to be a promising separation process thanks to the development of membrane materials with suitable resistance toward organic solvents. Among those materials, P84 polyimide membranes are currently the most used in OSN while PIM-1 membranes have recently attracted attention due to their high permeance in apolar solvents and alcohols. Both P84 and PIM-1 membranes have nanosized free volumes, and their separation performance is finely connected to polymer/solvent interactions. Consequently, modeling OSN membranes at the molecular scale is highly desirable in order to rationalize experimental observations and gain a deeper insight into the molecular mechanisms ruling solvent and solute permeation. A prerequisite for understanding solvent transport through OSN membranes is therefore to characterize the membrane/solvent interactions at the molecular level. For that purpose, we carried out molecular simulations of three different solvents, acetone, methanol, and toluene in contact with P84 and PIM-1 membranes. The solvent uptake by both membranes was found to be correlated to the degree of confinement of the solvent, the polymer swelling ability and polymer/solvent interactions. The translational dynamics of the solvent molecules in the PIM-1 membrane was found to be correlated with the solvent viscosity due to the relatively large pores of this membrane. That was not the case with the P84 membrane, which has a much denser structure than the PIM-1 membrane and for which it was observed that the translational dynamics of the confined solvent molecules was directly correlated to the affinity between the P84 polymer and the solvent.

Accelerating Discovery of High Fractional Free Volume Polymers from a Data-Driven Approach

As a fundamental structure characteristic in polymers, fractional free volume (FFV) plays an indispensable role in governing polymer properties and performance. However, the design of new high-FFV polymers is challenging. In this study, we report a data-driven approach and aim to accelerate the discovery of high-FFV polymers. First, a computational method is proposed to calculate FFV, and a two-step fragmentation method is developed to construct a fragment library for digital representation of polymer structures. Data mining is employed to identify promising fragments for high FFV. Subsequently, machine learning (ML) models are trained using a data set with 1683 polymers and their excellent transferability is demonstrated by out-of-sample predictions in another data set with 11,479 polymers. Finally, the ML models are used to screen ∼1 million hypothetical polymers, and 29,482 polymers with FFV > 0.2 are shortlisted; representative high-FFV polymers are validated by molecular simulations, and design strategies are highlighted. To further facilitate the discovery of new high-FFV polymers, we develop an online interactive platform https://ffv-prediction.herokuapp.com, which allows for rapid FFV predictions, given polymer structures. The data-driven approach in this study might advance the development of new high-FFV polymers and further explore quantitative structure-property relationships for polymers.