The field of polymer membrane design is primarily based on empirical observation, which limits discovery of new materials optimized for separating a given gas pair. Instead of relying on exhaustive experimental investigations, we trained a machine learning (ML) algorithm, using a topological, path-based hash of the polymer repeating unit. We used a limited set of experimental gas permeability data for six different gases in ~700 polymeric constructs that have been measured to date to predict the gas-separation behavior of over 11,000 homopolymers not previously tested for these properties. To test the algorithm’s accuracy, we synthesized two of the most promising polymer membranes predicted by this approach and found that they exceeded the upper bound for CO2/CH4 separation performance. This ML technique, which is trained using a relatively small body of experimental data (and no simulation data), evidently represents an innovative means of exploring the vast phase space available for polymer membrane design.
n-Alkane encapsulation experiments within dimeric octa-acid cavitand capsules in water reveal a succession of packing motifs from extended, to helical, to hairpin, to spinning top structures with increasing chain length. Here, we report a molecular simulation study of alkane conformational preferences within these host-guest assemblies to uncover the factors stabilizing distinct conformers. The simulated alkane conformers follow the trends inferred from H NMR experiments, while guest proton chemical shifts evaluated from Gauge Invariant Atomic Orbital calculations provide further evidence our simulations capture guest packing within these assemblies. Analysis of chain length and dihedral distributions indicates that packing under confinement to minimize nonpolar guest and host interior contact with water largely drives the transitions. Mean intramolecular distance maps and transfer free energy differences suggest the extended and helical motifs are members of a larger family of linear guest structures, for which the guest gauche population increases with increasing chain length to accommodate the chains within the complex. Breaks observed between the helical/hairpin and hairpin/spinning top motifs, on the other hand, indicate the hairpin and spinning top conformations are distinct from the linear family. Our results represent the first bridging of empirical and simulation data for flexible guests encapsulated within confined nanospaces, and constitute an effective strategy by which guest packing motifs within artificial or natural compartments can be rationalized and/or predicted a priori.
We show that coarse-grained molecular dynamics simulations do not capture experimental trends for the gas diffusion in matrix-free polymer-grafted nanoparticle-based membranes.
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