Multifunctional networks using local training rules

The mechanical properties of disordered networks can be significantly modified to exhibit unconventional responses by changing a small fraction of their bonds. One such response is a long distance 'allosteric' response where an applied local strain at one site of the system creates a localized output strain at a distant site. Previous work has relied on computer simulations to design and predict the response of such systems using a cost function where the response of the entire network to each bond removal is used at each step to determine which bond to prune. I will present a novel design approach that relies only on local stress measurements to incorporate allosteric responses. Instead of relying on computer simulations, we have an experimental method to measure local stress distributions using photoelastic measurements. This approach can be used to experimentally implement different pruning methods. In order to create an allosteric response, instead of completely removing a set of bonds, one can also soften a fraction of the network's bonds. By creating some bonds out of variable stiffness composites that can readily switch between stiff and soft states, we can activate a particular allosteric response in a system. We can also create allosteric networks that exhibit different functionalities by softening different sets of bonds. This work provides a path to understand and create adaptable and trainable allosteric metamaterials.