Ising model prediction and DPD simulation of engineered nanoparticle networks

Networked materials that mimic neuronal networks and exhibit synaptic hold promise for the development of new computing devices. We report the properties of 2D square, 2D hexagonal, 3D cubic and 3D close-packed hexagonal regular arrays of engineered nanoparticles (ENPs) interconnected by an emergent polymer network as a possible candidate for emulating neuronal networks. The network connectivity has been theoretically predicted using an Ising type model. Mean-field theory and Monte Carlo (MC) simulations are in good agreement in describing the thermodynamics of the model. We also use a coarse-grained molecular dynamics (CGMD) model to benchmark the Ising type model. Both models are consistent in predicting network links at varying temperature, volume fraction and E-field strength.