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We have pursued the use of polymer-networked engineered nanoparticles as a candidate material capable of retaining information or perhaps even processing information in some prescribed way. Such operations would be of use for the neuromorphic engineering of materials that can compute intrinsically—that is, that they are in no way subject to a von Neumann architecture—and they have been identified as autonomous computing materials. Using trajectories integrated to much longer time steps than previously observed, we can now confirm that the response of the polymer-networked engineered nanoparticle arrays are highly sensitive to external perturbations. That is, the specific internal connections around given nanopar- ticles can be assigned to states useful for information processing, and the variations in their physical properties can result in specific responses allowing the state to be read. Moreover, their resulting equilibrium properties also depend on such external driving, and hence are subject to control which is a minimal requirement for these materials to be candidates for autonomous computing. We also demonstrate that using long polymer chains can help regulate the networks structures by increasing the 1st nearest links and reducing other links. 

Polymer-networked nanoparticles are the basis for advanced materials useful wearable electron- ics, drug delivery, autonomous computing and other applications. To characterize and predict the physics and underlying mechanisms of the network connections in 2D and 3D engineered nanopar- ticle (ENP) arrays, we developed an analogous Potts model of 3-state sites. Together with dissipa- tive particle dynamics (DPD) simulations, we found that the network structures in polymer-linked nanoparticle assemblies are generally dominated by the number of nearest neighbors and not the topology of the lattice. When the E-field regulates the network connections, the links along the E-field direction always dominate the overall network structure.