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Evolution in time-varying environments naturally leads to adaptable biological systems that can easily switch functionalities. Advances in the synthesis of environmentally responsive materials therefore open up the possibility of creating a wide range of synthetic materials which can also be trained for adaptability. We consider high-dimensional inverse problems for materials where any particular functionality can be realized by numerous equivalent choices of design parameters. By periodically switching targets in a given design algorithm, we can teach a material to perform incompatible functionalities with minimal changes in design parameters. We exhibit this learning strategy for adaptability in two simulated settings: elastic networks that are designed to switch deformation modes with minimal bond changes and heteropolymers whose folding pathway selections are controlled by a minimal set of monomer affinities. The resulting designs can reveal physical principles, such as nucleation-controlled folding, that enable such adaptability. 

A particle raft floating on an expanding liquid substrate provides a macroscopic analog for studying material failure. The time scales in this system allow both particle-relaxation dynamics and rift formation to be resolved. In our experiments, a raft, an aggregate of particles, is stretched uniaxially by the expansion of the air–liquid interface on which it floats. Its failure morphology changes continuously with pulling velocity. This can be understood as a competition between two velocity scales: the speed of re-aggregation, in which particles relax towards a low-energy configuration determined by viscous and capillary forces, and the difference of velocity between neighboring particles caused by the expanding liquid surface area. This competition selects the cluster length, i.e. , the distance between adjacent rifts. A model based on this competition is consistent with the experimental failure patterns. 

The introduction of transient degrees of freedom into a system can lead to novel material design and training protocols that guide a system into a desired metastable state. In this approach, some degrees of freedom, which were not initially included in the system dynamics, are first introduced and subsequently removed from the energy minimization process once the desired state is reached. Using this conceptual framework, we create stable jammed packings that exist in exceptionally deep energy minima marked by the absence of low-frequency quasilocalized modes; this added stability persists in the thermodynamic limit. The inclusion of particle radii as transient degrees of freedom leads to deeper and much more stable minima than does the inclusion of particle stiffnesses. This is because particle radii couple to the jamming transition, whereas stiffnesses do not. Thus, different choices for the added degrees of freedom can lead to very different training outcomes. 

We describe a high-speed interferometric method, using multiple angles of incidence and multiple wavelengths, to measure the absolute thickness, tilt, the local angle between the surfaces, and the refractive index of a fluctuating transparent wedge. The method is well suited for biological, fluid and industrial applications.

 

We consider disordered solids in which the microscopic elements can deform plastically in response to stresses on them. We show that by driving the system periodically, this plasticity can be exploited to train in desired elastic properties, both in the global moduli and in local “allosteric” interactions. Periodic driving can couple an applied “source” strain to a “target” strain over a path in the energy landscape. This coupling allows control of the system’s response, even at large strains well into the nonlinear regime, where it can be difficult to achieve control simply by design.