Percolation-Induced Exponential Scaling in the Large Current Tails of Random Resistor Networks

Abstract

There is a renewed surge in percolation-induced transport properties of diverse nano-particle composites (cf. RSC Nanoscience & Nanotechnology Series, Paul O'Brien Editor-in-Chief). We note in particular a broad interest in nano-composites exhibiting sharp electrical property gains at and above percolation threshold, which motivated us to revisit the classical setting of percolation in random resistor networks but from a multiscale perspective. For each realization of random resistor networks above threshold, we use network graph representations and associated algorithms to identify and restrict to the percolating component, thereby preconditioning the network both in size and accuracy by filtering {\it a priori} zero current-carrying bonds. We then simulate many realizations per bond density and analyze scaling behavior of the complete current distribution supported on the percolating component. We first confirm the celebrated power-law distribution of small currents at the percolation threshold, and second we confirm results on scaling of the maximum current in the network that is associated with the backbone of the percolating cluster. These properties are then placed in context with global features of the current distribution, and in particular the dominant role of the large current tail that is most relevant for material science applications. We identify a robust, exponential large current tail that: 1. persists above threshold; 2. expands broadly over and dominates the current distribution at the expense of the vanishing power law scaling in the small current tail; and 3. by taking second moments, reproduces the experimentally observed power law scaling of bulk conductivity above threshold.