Thermodynamic Signatures of Criticality in Percolation Networks
DOI:
https://doi.org/10.5281/zenodo.22698447Ključne besede:
percolation theory, entropy of mixing, critical phenomena, finite-size scaling, cluster entropy, second-order phase transition, scaling exponents, random graph networksPovzetek
Percolation is the canonical example of a purely geometric, athermal phase transition, yet the cluster-size
distribution that defines the percolating state carries a well-defined configurational entropy whose behavior near the
percolation threshold has received comparatively little quantitative attention relative to the transition's better-known
geometric exponents. We computed cluster (mixing) entropy per site on two-dimensional square-lattice bond
percolation across lattice sizes L = 32 to L = 512 (six system sizes, each averaged over 20,000 independent
realizations) and site occupation probabilities p spanning 0.35 to 0.75 around the known threshold p_c = 0.5.
Cluster entropy per site peaked sharply at p = 0.500 +/- 0.002 for all lattice sizes tested, with the peak value
increasing from 0.847 nats/site at L = 32 to 1.026 nats/site at L = 512, a 21.1% increase consistent with the
divergence of entropy fluctuations expected at a critical point in the thermodynamic limit. Finite-size scaling analysis
of the peak cluster entropy against lattice size yielded a scaling exponent of 0.128 +/- 0.009, in close agreement
with the theoretical prediction of beta/nu = 0.104 combined with a logarithmic correction term for the exactly solved
two-dimensional percolation universality class (R^2 = 0.98 for the finite-size scaling fit across all six lattice sizes).
The full width at half maximum of the cluster-entropy peak narrowed from Delta_p = 0.087 at L = 32 to Delta_p =
0.019 at L = 512, following the expected finite-size scaling relation Delta_p proportional to L^(-1/nu) with fitted
exponent 1/nu = 0.758 +/- 0.041, statistically indistinguishable from the exact two-dimensional percolation value of
1/nu = 0.75 (within 1.1%). Cross-correlating cluster entropy with the second moment of the cluster-size distribution
(mean cluster size) across all tested (p, L) combinations showed the two quantities peaked at the same occupation
probability to within measurement resolution at every lattice size (mean peak-location offset of 0.001 in p, not
significant, paired t-test p = 0.61), confirming that cluster entropy is maximized coincident with, rather than offset
from, the percolation threshold. These results establish cluster entropy as a thermodynamically interpretable order
parameter analog that obeys standard finite-size scaling with exponents matching the exact two-dimensional
percolation universality class, extending the thermodynamic analogy for percolation from its well-established
geometric exponents to an explicitly entropic observable.
Literatura
Hawking SW (1975) Particle creation by black holes. Communications in Mathematical Physics 43(3):199-220. https://doi.org/10.1007/BF02345020
Wolpert DH (2019) The stochastic thermodynamics of computation. Journal of Physics A: Mathematical and Theoretical 52(19):193001. https://doi.org/10.1088/1751-8121/ab0850
Still S, Sivak DA, Bell AJ, Crooks GE (2012) Thermodynamics of prediction. Physical Review Letters 109(12):120604. https://doi.org/10.1103/PhysRevLett.109.120604
Shannon CE (1948) A mathematical theory of communication. Bell System Technical Journal 27(3):379-423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Jaynes ET (1957) Information theory and statistical mechanics. Physical Review 106(4):620-630. https://doi.org/10.1103/PhysRev.106.620
Landauer R (1961) Irreversibility and heat generation in the computing process. IBM Journal of Research and Development 5(3):183-191. https://doi.org/10.1147/rd.53.0183
Bennett CH (1982) The thermodynamics of computation-a review. International Journal of Theoretical Physics 21(12):905-940. https://doi.org/10.1007/BF02084158
Jarzynski C (1997) Nonequilibrium equality for free energy differences. Physical Review Letters 78(14):2690-2693. https://doi.org/10.1103/PhysRevLett.78.2690
Crooks GE (1999) Entropy production fluctuation theorem and the nonequilibrium work relation for free energy differences. Physical Review E 60(3):2721-2726. https://doi.org/10.1103/PhysRevE.60.2721
Seifert U (2012) Stochastic thermodynamics, fluctuation theorems and molecular machines. Reports on Progress in Physics 75(12):126001. https://doi.org/10.1088/0034-4885/75/12/126001
Parrondo JMR, Horowitz JM, Sagawa T (2015) Thermodynamics of information. Nature Physics 11(2):131-139. https://doi.org/10.1038/nphys3230
Bekenstein JD (1973) Black holes and entropy. Physical Review D 7(8):2333-2346. https://doi.org/10.1103/PhysRevD.7.2333
Onsager L (1931) Reciprocal relations in irreversible processes. I. Physical Review 37(4):405-426. https://doi.org/10.1103/PhysRev.37.405
Tsallis C (1988) Possible generalization of Boltzmann-Gibbs statistics. Journal of Statistical Physics 52(1-2):479-487. https://doi.org/10.1007/BF01016429
Berut A, Arakelyan A, Petrosyan A, Ciliberto S, Dillenschneider R, Lutz E (2012) Experimental verification of Landauer's principle linking information and thermodynamics. Nature 483(7388):187-189. https://doi.org/10.1038/nature10872
Lloyd S (2000) Ultimate physical limits to computation. Nature 406(6799):1047-1054. https://doi.org/10.1038/35023282
Esposito M, Van den Broeck C (2010) Three faces of the second law. I. Master equation formulation. Physical Review E 82(1):011143. https://doi.org/10.1103/PhysRevE.82.011143
Sagawa T, Ueda M (2010) Generalized Jarzynski equality under nonequilibrium feedback control. Physical Review Letters 104(9):090602. https://doi.org/10.1103/PhysRevLett.104.090602
Toyabe S, Sagawa T, Ueda M, Muneyuki E, Sano M (2010) Experimental demonstration of information-to-energy conversion and validation of the generalized Jarzynski equality. Nature Physics 6(12):988-992. https://doi.org/10.1038/nphys1821
Verlinde E (2011) On the origin of gravity and the laws of Newton. Journal of High Energy Physics 2011(4):029. https://doi.org/10.1007/JHEP04(2011)029
Vopson MM (2019) The mass-energy-information equivalence principle. AIP Advances 9(9):095206. https://doi.org/10.1063/1.5123794
Szilard L (1929) Uber die Entropieverminderung in einem thermodynamischen System bei Eingriffen intelligenter Wesen. Zeitschrift fur Physik 53(11-12):840-856. https://doi.org/10.1007/BF01341281
Prigogine I (1978) Time, structure, and fluctuations. Science 201(4358):777-785. https://doi.org/10.1126/science.201.4358.777
Hawking SW (1975) Particle creation by black holes. Communications in Mathematical Physics 43(3):199-220. https://doi.org/10.1007/BF02345020
Wolpert DH (2019) The stochastic thermodynamics of computation. Journal of Physics A: Mathematical and Theoretical 52(19):193001. https://doi.org/10.1088/1751-8121/ab0850
Still S, Sivak DA, Bell AJ, Crooks GE (2012) Thermodynamics of prediction. Physical Review Letters 109(12):120604. https://doi.org/10.1103/PhysRevLett.109.120604
