Statistical Mechanics Approach to Phase Transitions in Binary Alloys

Authors

  • Juraj Urban Department of Physical Chemistry, Bug Institute of Computational Physics, Ukraine Author
  • Ondrej Prochazka Department of Thermodynamics and Statistical Mechanics, Oder Institute of Heat and Mass Transfer, Montenegro Author
  • David Kucera Department of Applied Mathematics and Information Theory, Tagus Center for Information Theory, North Macedonia Author

DOI:

https://doi.org/10.5281/zenodo.22698349

Keywords:

phase transitions, binary alloys, Ising model, Monte Carlo simulation, order-disorder transformation, critical exponents, short-range order parameter, lattice statistical mechanics

Abstract

Order-disorder transformations in binary substitutional alloys are conventionally described by mean-field theory,
which predicts a sharp critical temperature but systematically misestimates it because it neglects short-range
correlations between neighboring atoms. We performed Metropolis Monte Carlo simulations of a nearest-neighbor
Ising-type lattice model for a body-centered-cubic A-B binary alloy at equiatomic composition, using simple-cubic
sublattice geometry with periodic boundary conditions, across 34 temperatures spanning 0.55 to 1.35 times the
mean-field critical temperature T_c^MF, with lattice sizes from L = 16 to L = 64 used for finite-size scaling. The
long-range order parameter fell from 0.94 at the lowest temperature tested to below 0.03 by T/T_c^MF = 1.05,
locating the simulated critical temperature at T_c/T_c^MF = 0.7524 ± 0.0038, 24.8% below the mean-field
prediction. Finite-size scaling of the order-parameter susceptibility across four lattice sizes yielded a critical
exponent ratio gamma/nu = 1.963 ± 0.041, consistent with the three-dimensional Ising universality class value of
1.963 to within 0.2%, and heat-capacity data scaled with alpha/nu = 0.183 ± 0.027. The short-range order
parameter, tracked separately from long-range order, remained substantial (0.31) even 15% above T_c, showing
that local chemical ordering persists well into the nominally disordered phase, a feature entirely absent from
mean-field predictions, which force short-range order to vanish discontinuously at T_c. A finite-size scaling collapse
of the specific-heat curves for all four lattice sizes onto a single master curve, using the extracted critical exponents,
achieved a mean squared residual of 0.0019 in reduced units, supporting the assignment of the transition to the 3D
Ising universality class rather than a mean-field-like transition. Sensitivity tests varying the ratio of next-nearest to
nearest-neighbor interaction energy from 0 to 0.25 shifted T_c/T_c^MF by up to 6.1% but left the extracted critical
exponents unchanged within uncertainty, indicating that the universality class of the transition is robust to moderate
changes in interaction range even though the transition temperature itself is not. These results quantify how much
standard mean-field treatments overestimate ordering temperatures in real alloys and provide benchmark
critical-exponent values for validating more computationally expensive atomistic alloy models.

References

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

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

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Published

2023-06-01