Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
2010-06-07
Physics
Condensed Matter
Disordered Systems and Neural Networks
13+epsilon pages
Scientific paper
We study the solutions of Von Neumann's expanding model with reversible processes for an infinite reaction network. We show that, contrary to the irreversible case, the solution space need not be convex in contracting phases (i.e. phases where the concentrations of reagents necessarily decrease over time). At optimality, this implies that, while multiple dynamical paths of global contraction exist, optimal expansion is achieved by a unique time evolution of reaction fluxes. This scenario is investigated in a statistical mechanics framework by a replica symmetric theory. The transition from a non-convex to a convex solution space, which turns out to be well described by a phenomenological order parameter (the fraction of unused reversible reactions) is analyzed numerically.
Figliuzzi Matteo
Marsili Margherita
Martino Alessandro de
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