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We show that the polymer-grafted nanoparticles (NPs) initially welldispersed in a polymer matrix segregate to the free surface of a film upon thermal annealing in the one-phase region of the phase diagram because the grafted polymer has a lower surface energy than the matrix polymer. Using a combination of atomic force microscopy, transmission electron microscopy, and Rutherford backscattering spectrometry, the evolution of the poly(methyl methacrylate)-grafted silica NP (PMMA NP) surface excess in 25/75 wt % PMMA NP/poly(styrene-ranacrylonitrile) films is observed as a function of annealing time at 150 °C (T < TLCST). The temporal growth of the surface excess is interpreted as a competition between entropic contributions, surface energy differences of the constituents, and the Flory−Huggins interaction parameter, χ. For the first time in a miscible polymer nanocomposite mixture, quantitative comparisons of NP surface segregation are made with the predictions of theory derived for analogous polymer blends. These studies provide insight for designing polymer nanocomposite films with advantageous surface properties such as wettability and hardness and motivate the need for developing rigorous models that capture complex polymer nanocomposite phase behaviors. 

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 CO 2 /CH 4 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.