Pattern Recognition with Active Matter: Designing “Materials that Compute”
Using theoretical and computational modeling, we design an active materials system that can autonomously transduce chemical, mechanical and electrical energy to perform a computational task in a self-organized manner, without the need for external electrical power sources. Each unit in this system integrates a self-oscillating gel, which undergoes the Belousov-Zhabotinsky (BZ) reaction, with an overlaying piezoelectric (PZ) cantilever. The chemo-mechanical oscillations of the BZ gels deflect the piezoelectric layer, which consequently generates a voltage across the material. When these BZ-PZ units are connected in series by electrical wires, the oscillations of these units become synchronized across the network, with the mode of synchronization depending on the polarity of the piezoelectric. Taking advantage of this synchronization behavior, we show that the network of coupled BZ-PZ oscillators can perform pattern recognition tasks. We define the “stored” pattern as a set of polarities of the individual BZ-PZ units, and the “input” patterns are coded through the initial phase of the oscillations imposed on these units. The results of the computational modeling show that the “input” pattern closest to the “stored” pattern exhibits the fastest convergence time to the stable synchronization behavior. In this way, the networks of coupled BZ-PZ oscillators achieve pattern recognition. Further, we show that the convergence time to the stable synchronization provides a robust measure of the degree of match between the input and stored patterns. Through these studies, we establish experimentally realizable design rules for creating “materials that compute”.