Percolation-induced exponential scaling in the large current tails of random resistor networks
Feng Shi, Simi Wang, Peter J. Mucha, M. Gregory Forest

TL;DR
This study investigates the current distribution in random resistor networks above the percolation threshold, revealing a persistent exponential tail in large currents that influences overall conductivity and material properties.
Contribution
It introduces a multiscale analysis approach to identify and analyze the large current tail in resistor networks, highlighting its exponential nature and impact on conductivity.
Findings
Confirmed power-law distribution of small currents at threshold
Identified exponential tail dominating large currents
Reproduced power-law scaling of bulk conductivity
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…
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Taxonomy
TopicsGraphene research and applications · Graph theory and applications · Complex Network Analysis Techniques
