  Nanoparticles interconnected by insulating organic molecules exhibit
nonlinear switching behavior at low temperatures. By assembling these switches
into a network and manipulating charge transport dynamics through surrounding
electrodes, the network can be reconfigurably functionalized to act as any
Boolean logic gate. This work introduces a kinetic Monte Carlo-based simulation
tool, applying established principles of single electronics to model charge
transport dynamics in nanoparticle networks. We functionalize nanoparticle
networks as Boolean logic gates and assess their quality using a fitness
function. Based on the definition of fitness, we derive new metrics to quantify
essential nonlinear properties of the network, including negative differential
resistance and nonlinear separability. These nonlinear properties are crucial
not only for functionalizing the network as Boolean logic gates but also when
our networks are functionalized for brain-inspired computing applications in
the future. We address fundamental questions about the dependence of fitness
and nonlinear properties on system size, number of surrounding electrodes, and
electrode positioning. We assert the overall benefit of having more electrodes,
with proximity to the network's output being pivotal for functionality and
nonlinearity. Additionally, we demonstrate a optimal system size and argue for
breaking symmetry in electrode positioning to favor nonlinear properties.
