Local rules for fabricating allosteric networks

Abstract

Mechanical properties of disordered networks can be significantly tailored by modifying a small fraction of their bonds. This procedure has been used to design and build mechanical metamaterials with a variety of responses. A long-range 'allosteric' response, where a localized input strain at one site gives rise to a localized output strain at a distant site, has been of particular interest. This work presents a novel approach to incorporating allosteric responses in experimental systems by pruning disordered networks $\text{in-situ}$. 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. It is not feasible to follow such a design protocol in experiments where one has access only to local response at each site. This paper presents design algorithms that allow determination of what bonds to prune based purely on the local stresses in the network without employing a cost function; using only local information, allosteric networks are designed in simulations and then built out of real materials. The results show that some pruning strategies work better than others when translated into an experimental system. A method is presented to measure local stresses experimentally in disordered networks. This approach is then used to implement pruning methods to design desired responses $\text{in-situ}$. Results from these experiments confirm that the pruning methods are robust and work in a real laboratory material.

References in corpus (12)

• Jamming at Zero Temperature and Zero Applied Stress: the Epitome of Disorder
• Designing allostery-inspired response in mechanical networks
• Auxetic metamaterials from disordered networks
• Architecture and Co-Evolution of Allosteric Materials
• Spanning the Scales of Granular Materials: Microscopic Force Imaging
• Directed aging, memory and Nature's greed
• Rigidity Loss in Disordered Systems: Three Scenarios
• The limits of multifunctionality in tunable networks
• Supervised learning in physical networks: From machine learning to learning machines
• Physical model of the genotype-to-phenotype map of proteins
• Ideal isotropic auxetic networks from random networks
• Linking microscopic and macroscopic response in disordered solids

Cited by in corpus (8)

• Learning without neurons in physical systems
• Demonstration of Decentralized, Physics-Driven Learning
• Machine Learning Without a Processor: Emergent Learning in a Nonlinear Electronic Metamaterial
• Desynchronous Learning in a Physics-Driven Learning Network
• The Physical Effects of Learning
• Learning to self-fold at a bifurcation
• Training Coupled Phase Oscillators as a Neuromorphic Platform using Equilibrium Propagation
• Physical learning beyond the quasistatic limit