References
- Hill TL. An introduction to statistical thermodynamics. New York: Dover; 1986.
- Porter DA, Easterling KE, Sherif M. Phase transformations in metals and alloys. Boca Raton (FL): CRC Press; 2009.
- Landau DP, Binder K. A guide to Monte Carlo simulations in statistical physics. Cambridge: Cambridge University Press; 2000.
- De Fontaine D. Cluster approach to order-disorder transformations in alloys. In: Ehrenreich H, Turnbull D, editors. Solid State Physics. Academic Press; 1994. p. 33–176. Available from: http://www.sciencedirect.com/science/article/pii/S0081194708606396.
- William D, Callister J. Materials science and engineering: an introduction. 7th ed. New York: John Wiley; 2007.
- Frenkel D, Smit B. Understanding molecular simulation: from algorithms to applications. New York: Academic Press; 1996.
- Allen MP, Tildesley DJ. Computer simulation of liquids. Oxford: Oxford Science Publications; 1989.
- Arif I, Agrahari G, Gautam AK, et al. Inferring layer-by-layer composition in Au-Ag nanoparticles using a combination of X-ray photoelectron spectroscopy and Monte Carlo simulations. Surf Sci. 2020;691:121503.
- Divi S, Chatterjee A. Understanding segregation behavior in AuPt, NiPt, and AgAu bimetallic nanoparticles using distribution coefficients. J Phys Chem C. 2016;120:27296–27306 [Internet] [cited 2017 Jul 18]. Available from: http://pubs.acs.org/doi/abs/10. 1021/acs.jpcc.6b08325.
- Divi S, Chatterjee A. Generalized nano-thermodynamic model for capturing size-dependent surface segregation in multi-metal alloy nanoparticles. RSC Adv. 2018;8:10409–10424.
- Agrahari G, Chatterjee A. Thermodynamic calculations using reverse Monte Carlo. Phys Rev E. 2021;104:044129.
- Wikfeldt KT, Leetmaa M, Ljungberg MP, et al. On the range of water structure models compatible with X-ray and neutron diffraction data. J Phys Chem B. 2009;113:6246–6255.
- Harsányi I, Pusztai L. Hydration structure in concentrated aqueous lithium chloride solutions: a reverse Monte Carlo based combination of molecular dynamics simulations and diffraction data. J Chem Phys. 2012;137:204503.
- Howe MA, Mcgreevy RL, Pusztai L, et al. Determination of three body correlations in simple liquids by RMC modelling of diffraction data. Phys Chem Liq. 1993;25:205–241.
- Veglio N, Bermejo FJ, Pardo LC, et al. Direct experimental assessment of the strength of orientational correlations in polar liquids. Phys Rev E. 2005; 72: 031502
- McGreevy RL, Pusztai L. The structure of molten salts. Proc R Soc London Ser A Math Phys Sci. 1990;430:241–261.
- Kaban I, Jóvári P, Stoica M, et al. Topological and chemical ordering in Co 43 Fe 20 Ta 5.5 B 31.5 metallic glass. Phys Rev B; 79: 212201.
- Keen DA, McGreevy RL. Structural modelling of glasses using reverse Monte Carlo simulation. Nature. 29 March 1990;344:423–425.
- Keen DA, Tucker MG, Dove MT. Reverse Monte Carlo modelling of crystalline disorder. J Phys Condens Matter. 2005;17:S15–S22.
- Allen MP, Tildesley DJ. Computer simulation of liquids: second edition. London: Oxford University Press; 2017; p. 1–626.
- Rogal J, Reuter K. Ab initio atomistic thermodynamics for surfaces: a primer. Exp model simul gas-surface interact react flows hypersonic flights [internet]. Neuilly-sur- Seine. France: RTO: Defense Technical Information Center; 2006; p. 2-1-2–18. Available from: https://apps.dtic.mil/sti/pdfs/ADA476575.pdf.
- Agrahari G, Chatterjee A. Speed-up of Monte Carlo simulations by preparing starting off-lattice structures that are close to equilibrium. J Chem Phys. 2020;152:44102. [Internet] Available from: DOI:10.1063/1.5131303.
- McGreevy RL. Reverse Monte Carlo modelling. J Phys Condens Matter. 2001;13:R877–R913. [Internet] Available from: DOI:10.1088/0953-8984/13/46/201.
- McGreevy RL, Pusztai L. Reverse Monte Carlo simulation: a new technique for the determination of disordered structures. Mol Simul. 1988;1:359–367 [Internet] Available from: DOI:10.1080/08927028808080958.
- Bousige C, Boţan A, Ulm FJ, et al. Optimized molecular reconstruction procedure combining hybrid reverse Monte Carlo and molecular dynamics. J Chem Phys. 2015;142:114112.
- Opletal G, Petersen T, O’Malley B, et al. Hybrid approach for generating realistic amorphous carbon structure using metropolis and reverse Monte Carlo. Mol Simul. 2002;28:927–938.
- Tupy SA, Karim AM, Bagia C, et al. Correlating ethylene glycol reforming activity with in situ EXAFS detection of Ni segregation in supported NiPt bimetallic catalysts. ACS Catal. 2012;2:2290–2296. [Internet] Available from: DOI:10.1021/cs3004227.
- Van Kampen NG. Stochastic processes in physics and chemistry: North-Holland personal library; 1992.
- Chatterjee A, Vlachos DG. Multiscale spatial Monte Carlo simulations: multigriding, computational singular perturbation, and hierarchical stochastic closures. J Chem Phys. 2006;124:64110.