Phase transitions in large atomic clusters. Computer modeling
DOI (Low Temperature Physics):
https://doi.org/10.1063/10.0042170Ключові слова:
cluster, melting temperature, Monte Carlo modeling, mesoscopic rangeАнотація
Розроблено модифікований метод Монте-Карло, який імітує фазові переходи в атомних кластерах. Вперше застосовано цей метод для отримання за допомогою комп’ютерного моделювання залежностей властивостей кластера аргону від розміру в мезоскопічному діапазоні розмірів від 2000 до 12000 атомів та в діапазоні температур 20–80 K. Для цих температур було розраховано термодинамічні функції та знайдено рівноважний фазовий стан. Два критерії, такі як температура плавлення та середня міжатомна відстань, відповідно показують, що теплові властивості кластера наближаються до властивостей макроскопічного тіла при розмірі кластера порядка 10000 атомів. Запропонований метод дозволяє уникнути експоненціального зростання необхідної кількості кроків метода Монте-Карло з розміром кластера, тим самим експоненціально зменшуючи необхідний час моделювання.
Посилання
clusters, in: Handbook of Nanophase and Nanostructured Materials, edited by, Z. Wang, Y. Liu, and Z. Zhang (Springer, Boston, MA., 2003).
R. S. Berry and B. M. Smirnov, “Phase transitions in various kinds of clusters,” Phys.-Usp. 52, 137 (2009). https://doi.org/10.3367/UFNe.0179.200902b.0147
R. S. Berry and B. M. Smirnov, “Phase transitions in simple clusters,” JETP 100, 1129 (2005). https://doi.org/10.1134/1.1995797
B. M. Smirnov, “Clusters and phase transitions,” Phys. Usp. 50, 354 (2007). https://doi.org/10.1070/PU2007v050n04ABEH006235
B. M. Smirnov, in Clusters and Small Particles, in: Gases and Plasmas (Springer-Verlag, New York, 2000).
B. M. Smirnov, “Melting of clusters with pair interaction of atoms,” Phys. Usp. 37, 1079 (1994). https://doi.org/10.1070/PU1994v037n11ABEH000053
Th. M. Soinia and N. Rösch, “Size-dependent properties of transition metal clusters: From molecules to crystals and surfaces — computational studies with the program ParaGauss,” Phys. Chem. Chem. Phys. 17, 28463 (2015). https://doi.org/10.1039/C5CP04281J
S. Bani, P. S. Dutta, S. Manna, and S. Sankaranarayanan, “Development of a machine learning potential to study the structure and thermodynamics of nickel nanoclusters,” J. Phys. Chem. A 128, 47 (2024).
V. Yanovsky, M. Kopp, and M. Ratner, “Evolution of gas-filled pore in bounded particles,” Functional Mater. 26, 131 (2019). https://doi.org/10.15407/fm26.01.131
E. S. Hoffenberg, A. Khrabry, Yu. Barsukov, I. D. Kaganovich, and D. B. Graves, Types of Size-Dependent Melting in Fe Nanoclusters: A Molecular Dynamics Study (2024). arXiv:2409.02293 [physics.atm–clus].
H. Duana, F. Ding, R. Arne, A. R. Harutyunyanc, S. Curtarolod, and K. Bolton, “Size dependent melting mechanisms of iron nanoclusters,” Chem. Phys. 333, 57 (2007). https://doi.org/10.1016/j.chemphys.2007.01.005
R. V. Chamberlin and S. M. Lindsay, “Nanothermodynamics: There’s plenty of room on the inside,” Nanomaterials 14, 1828 (2024). https://doi.org/10.3390/nano14221828
A. V. Zhukov, A. S. Kraynyukova, and J. Cao, “On the thermodynamics of the liquid–solid transition in a small cluster,” Phys. Lett. A 364, 329 (2007). https://doi.org/10.1016/j.physleta.2006.12.004
B. I. Loukhovitski, A. V. Pelevkin, and A. S. Sharipov, “Toward size-dependent thermodynamics of nanoparticles from quantum chemical calculations of small atomic clusters,” Phys. Chem. Chem. Phys. 21, 13130 (2022). https://doi.org/10.1039/D2CP01672A
J. Pilla, G. Villani, and C. Brangian, “Energy and entropy barriers of two-level systems in argon clusters: An energy landscape approach,” Phys. Rev. B 60, 3200 (1999). https://doi.org/10.1103/PhysRevB.60.3200
M. Brack, S. Creagh, P. Meier, S. M. Reimann, and M. Seidl, Semiclassical methods for the description of large metal clusters, in: Large Clusters of Atoms and Molecules, NATO ASI Series, edited by, T. P. Martin (Springer, Dordrecht, 1996), p. 313.
D. J. Wales and J. P. K. Doye, Theoretical predictions of structure and thermodynamics in the large cluster regime, in: edited by, T. P. Martin, Large Clusters of Atoms and Molecules, NATO ASI Series (Springer, Dordrecht, 1996), p. 241.
O. Exper Echt, Convergence of cluster properties towards bulk behavior: How large is large?, in: edited by, T. P. Martin, Large Clusters of Atoms and Molecules, NATO ASI Series (Springer, Dordrecht, 1996), p. 221.
M. A. Ratner and V. V. Yanovsky, “Monte carlo modeling of atomic clusters in mesoscopic range,” Funct. Mater. 31, 225 (2024).
M. A. Ratner and V. V. Yanovsky, “Pecularities of pore relaxation in nanoclusters,” Funct. Mater. 23, 612 (2016). https://doi.org/10.15407/fm23.04.427
J. A. Lee and F. Barker, “Abraham theory and monte carlo simulation of physical clusters in the imperfect vapor,” J. Chem. Phys. 58, 3166 (1973). https://doi.org/10.1063/1.1679638
D. Frenkel and B. Smit, Understanding Molecular Simulation (Academic Press, 2001). ISBN 0-12-267351-4.
E. Paquet and H. L. Viktor, “Molecular dynamics, monte-carlo simulations, and langevin dynamics: A computational review,” Biomed Res Int. 2015, 183918 (2015). https://doi.org/10.1155/2015/183918
B. A. Berg, Markov Chain Monte Carlo Simulations and Their Statistical Analysis, (World Scientific, Singapore MATH, 2004).
E. Luijten, Introduction to Cluster Monte Carlo Algorithms (2007), Vol. 1, p. 13. https://doi.org/10.1007/3-540-35273-2_1
D. P. Landau and K. Binder, A Guide to Monte-Carlo Simulations in Statistical Physics, 4th ed. (Cambridge University Press, 2014), p. 519.
A. Z. Panagiotopoulos, “Direct determination of phase coexistence properties of fluids by monte carlo simulation in a new ensemble,” Mol. Phys. 61, 813 (1987). https://doi.org/10.1080/00268978700101491
K. Binder and D. W. Heermann, Monte Carlo Simulation in Statistical Physics: An Introduction, 4th ed (Springer, Berlin, 2002).
D. M. Ceperley, Path integral monte carlo methods for fermions, in: Monte Carlo and Molecular Dynamics of Condensed Matter Systems. Societa Italiana di Fisica, edited by, K. Binder and G. Ciccotti, (Bologna, 1996), p. 445.
N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, “Equation of state calculations by fast computing machines,” J. Chem. Phys. 21, 1087 (1953). https://doi.org/10.1063/1.1699114
V. V. Nauchitel and A. J. Pertsin, “A monte carlo study of the structure and thermodynamic behaviour of small lennard-jones clusters,” Mol. Phys. 40, 1341 (1980). https://doi.org/10.1080/00268978000102331
X. Xu and F. Wang, “Monte carlo simulation of cluster growth at different temperatures,” Chin, J. Comput. Phys. 26, 758 (2009).
P. Tepesch, M. Asta, and C. Gerbrand “Computation of configurational entropy using monte carlo probabilities in cluster-variation method entropy expressions,” Modelling Simul. Mater. Sci. Eng. 6, 787 (1998). https://doi.org/10.1088/0965-0393/6/6/009
D. J. Wales and J. P. K. Doye, “Global optimization by basin-hopping and the lowest energy structures of lennard-jones clusters containing up to 110 atoms,” J. Phys. Chem. A 101, 5111 (1997). https://doi.org/10.1021/jp970984n
G. R. Gustavo and J. L. F. Da Silva, “A revised basin-hopping monte carlo algorithm for structure optimization of clusters and nanoparticles,” J. Chem. Inf. Model. 53, 2282 (2013). https://doi.org/10.1021/ci400224z
V. V. Nauchitel and A. J. Pertsin, “A monte carlo study of the structure and thermodynamic behaviour of small lennard-jones clusters,” Mol. Phys. 40, 1341 (1980). https://doi.org/10.1080/00268978000102331
E. R. Dobbs and G. O. Jones, “Theory and properties of solid argon,” Rep. Prog. Phys. 20, 516 (1957). https://doi.org/10.1088/0034-4885/20/1/309
