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Nanoparticles interconnected by insulating organic molecules exhibitnonlinear switching behavior at low temperatures. By assembling these switchesinto a network and manipulating charge transport dynamics through surroundingelectrodes, the network can be reconfigurably functionalized to act as anyBoolean logic gate. This work introduces a kinetic Monte Carlo-based simulationtool, applying established principles of single electronics to model chargetransport dynamics in nanoparticle networks. We functionalize nanoparticlenetworks as Boolean logic gates and assess their quality using a fitnessfunction. Based on the definition of fitness, we derive new metrics to quantifyessential nonlinear properties of the network, including negative differentialresistance and nonlinear separability. These nonlinear properties are crucialnot only for functionalizing the network as Boolean logic gates but also whenour networks are functionalized for brain-inspired computing applications inthe future. We address fundamental questions about the dependence of fitnessand nonlinear properties on system size, number of surrounding electrodes, andelectrode positioning. We assert the overall benefit of having more electrodes,with proximity to the network's output being pivotal for functionality andnonlinearity. Additionally, we demonstrate a optimal system size and argue forbreaking symmetry in electrode positioning to favor nonlinear properties.

A kinetic Monte Carlo Approach for Boolean Logic Functionality in Gold Nanoparticle Networks