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Mechanical properties of polymers with polymer grafted nanoparticles. Role of nanoparticle morphology is studied critically 

Soft materials are usually defined as materials made of mesoscopic entities, often self-organised, sensitive to thermal fluctuations and to weak perturbations. Archetypal examples are colloids, polymers, amphiphiles, liquid crystals, foams. The importance of soft materials in everyday commodity products, as well as in technological applications, is enormous, and controlling or improving their properties is the focus of many efforts. From a fundamental perspective, the possibility of manipulating soft material properties, by tuning interactions between constituents and by applying external perturbations, gives rise to an almost unlimited variety in physical properties. Together with the relative ease to observe and characterise them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of them relevant as well to hard condensed matter systems. Understanding the emerging properties from mesoscale constituents still poses enormous challenges, which have stimulated a wealth of new experimental approaches, including the synthesis of new systems with, e.g. tailored self-assembling properties, or novel experimental techniques in imaging, scattering or rheology. Theoretical and numerical methods, and coarse-grained models, have become central to predict physical properties of soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations. This Roadmap intends to give a broad overview of recent and possible future activities in the field of soft materials, with experts covering various developments and challenges in material synthesis and characterisation, instrumental, simulation and theoretical methods as well as general concepts. 

When polymer–nanoparticle (NP) attractions are sufficiently strong, a bound polymer layer with a distinct dynamic signature spontaneously forms at the NP interface. A similar phenomenon occurs near a fixed attractive substrate for thin polymer films. While our previous simulations fixed the NPs to examine the dilute limit, here, we allow the NP to move. Our goal is to investigate how NP mobility affects the signature of the bound layer. For small NPs that are relatively mobile, the bound layer is slaved to the motion of the NP, and the signature of the bound layer relaxation in the intermediate scattering function essentially disappears. The slow relaxation of the bound layer can be recovered when the scattering function is measured in the NP reference frame, but this process would be challenging to implement in experimental systems with multiple NPs. Instead, we use the counterintuitive result that the NP mass affects its mobility in the nanoscale limit, along with the more expected result that the bound layer increases the effective NP mass, to suggest that the signature of the bound polymer manifests as a change in NP diffusivity. These findings allow us to rationalize and quantitatively understand the results of recent experiments focused on measuring NP diffusivity with either physically adsorbed or chemically end-grafted chains. 

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.