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Journal articles on the topic 'Grand Canonical Monte Carlo simulations'

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Author: Grafiati

Published: 10 December 2022

Last updated: 6 September 2023

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1

Ren, Ruichao, C. J. O'keeffe, and G. Orkoulas. "Sequential updating algorithms for grand canonical Monte Carlo simulations." Molecular Physics 105, no. 2-3 (January 20, 2007): 231–38. http://dx.doi.org/10.1080/00268970601143341.

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2

Kindt, James T. "Pivot-coupled grand canonical Monte Carlo method for ring simulations." Journal of Chemical Physics 116, no. 15 (April 15, 2002): 6817–25. http://dx.doi.org/10.1063/1.1461359.

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3

Koibuchi, Hiroshi, Nobuyuki Kusano, Atsusi Nidaira, Komei Suzuki, and Mitsuru Yamada. "Grand canonical Monte Carlo simulations of elastic membranes with fluidity." Physics Letters A 319, no. 1-2 (December 2003): 44–52. http://dx.doi.org/10.1016/j.physleta.2003.10.018.

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4

Woo, Hyung-June, Aaron R. Dinner, and Benoît Roux. "Grand canonical Monte Carlo simulations of water in protein environments." Journal of Chemical Physics 121, no. 13 (October 2004): 6392–400. http://dx.doi.org/10.1063/1.1784436.

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5

Cracknell, Roger F. "On the Sampling Method for Grand Canonical Monte Carlo Simulations." Molecular Simulation 13, no. 3 (September 1994): 235–40. http://dx.doi.org/10.1080/08927029408021987.

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6

Hansen, Niels, Sven Jakobtorweihen, and Frerich J. Keil. "Reactive Monte Carlo and grand-canonical Monte Carlo simulations of the propene metathesis reaction system." Journal of Chemical Physics 122, no. 16 (April 22, 2005): 164705. http://dx.doi.org/10.1063/1.1884108.

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7

Sachin Krishnan, T. V., Sovan L. Das, and P. B. Sunil Kumar. "Transition from curvature sensing to generation in a vesicle driven by protein binding strength and membrane tension." Soft Matter 15, no. 9 (2019): 2071–80. http://dx.doi.org/10.1039/c8sm02623h.

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8

Pham, Tony, Katherine A. Forrest, Adam Hogan, Keith McLaughlin, Jonathan L. Belof, Juergen Eckert, and Brian Space. "Simulations of hydrogen sorption in rht-MOF-1: identifying the binding sites through explicit polarization and quantum rotation calculations." J. Mater. Chem. A 2, no. 7 (2014): 2088–100. http://dx.doi.org/10.1039/c3ta14591c.

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Grand canonical Monte Carlo simulations of H₂ sorption were performed in the metal–organic framework rht-MOF-1. The binding sites were revealed by combining simulation and inelastic neutron scattering data.

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9

Ruff, Imre, András Baranyai, Gábor Pálinkás, and Karl Heinzinger. "Grand canonical Monte Carlo simulation of liquid argon." Journal of Chemical Physics 85, no. 4 (August 15, 1986): 2169–77. http://dx.doi.org/10.1063/1.451110.

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10

Fábián, Balázs, Sylvain Picaud, Pál Jedlovszky, Aurélie Guilbert-Lepoutre, and Olivier Mousis. "Ammonia Clathrate Hydrate As Seen from Grand Canonical Monte Carlo Simulations." ACS Earth and Space Chemistry 2, no. 5 (March 9, 2018): 521–31. http://dx.doi.org/10.1021/acsearthspacechem.7b00133.

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11

Gotzias, A., H. Heiberg-Andersen, M. Kainourgiakis, and Th Steriotis. "Grand canonical Monte Carlo simulations of hydrogen adsorption in carbon cones." Applied Surface Science 256, no. 17 (June 2010): 5226–31. http://dx.doi.org/10.1016/j.apsusc.2009.12.108.

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12

Moghaddam, Sarvin, and Athanassios Z. Panagiotopoulos. "Determination of second virial coefficients by grand canonical Monte Carlo simulations." Fluid Phase Equilibria 222-223 (August 2004): 221–24. http://dx.doi.org/10.1016/j.fluid.2004.06.018.

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13

Singh, Abhishek K., Jianxin Lu, Rachel S. Aga, and Boris I. Yakobson. "Hydrogen Storage Capacity of Carbon-Foams: Grand Canonical Monte Carlo Simulations." Journal of Physical Chemistry C 115, no. 5 (November 30, 2010): 2476–82. http://dx.doi.org/10.1021/jp104889a.

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14

Cheong, Daniel W., and Athanassios Z. Panagiotopoulos *. "Phase behaviour of polyampholyte chains from grand canonical Monte Carlo simulations." Molecular Physics 103, no. 21-23 (November 10, 2005): 3031–44. http://dx.doi.org/10.1080/00268970500186045.

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15

Yau, Danny H. L., Steven Y. Liem, and Kwong‐Yu Chan. "A contact cavity‐biased method for grand canonical Monte Carlo simulations." Journal of Chemical Physics 101, no. 9 (November 1994): 7918–24. http://dx.doi.org/10.1063/1.468218.

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16

Glover, Joseph, and Elena Besley. "Pore-filling contamination in metal–organic frameworks." Physical Chemistry Chemical Physics 20, no. 36 (2018): 23616–24. http://dx.doi.org/10.1039/c8cp04769c.

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17

Soroush Barhaghi, Mohammad, Korosh Torabi, Younes Nejahi, Loren Schwiebert, and Jeffrey J. Potoff. "Molecular exchange Monte Carlo: A generalized method for identity exchanges in grand canonical Monte Carlo simulations." Journal of Chemical Physics 149, no. 7 (August 21, 2018): 072318. http://dx.doi.org/10.1063/1.5025184.

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18

Peng, Xuan, Surendra Kumar Jain, Jayant Kumar Singh, Anqi Liu, and Qibing Jin. "Formation patterns of water clusters in CMK-3 and CMK-5 mesoporous carbons: a computational recognition study." Physical Chemistry Chemical Physics 20, no. 25 (2018): 17093–104. http://dx.doi.org/10.1039/c8cp01887a.

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19

Agrawal, Ankit, Mayank Agrawal, Donguk Suh, Yunsheng Ma, Ryotaro Matsuda, Akira Endo, Wei-Lun Hsu, and Hirofumi Daiguji. "Molecular simulation study on the flexibility in the interpenetrated metal–organic framework LMOF-201 using reactive force field." Journal of Materials Chemistry A 8, no. 32 (2020): 16385–91. http://dx.doi.org/10.1039/c9ta12065c.

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20

Pinto, O. A., P. M. Pasinetti, A. J. Ramirez-Pastor, and F. D. Nieto. "The adsorption of a mixture of particles with non-additive interactions: a Monte Carlo study." Physical Chemistry Chemical Physics 17, no. 5 (2015): 3050–58. http://dx.doi.org/10.1039/c4cp04428b.

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21

Clark, Matthew, Frank Guarnieri, Igor Shkurko, and Jeff Wiseman. "Grand Canonical Monte Carlo Simulation of Ligand−Protein Binding." Journal of Chemical Information and Modeling 46, no. 1 (January 2006): 231–42. http://dx.doi.org/10.1021/ci050268f.

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22

Herzog, G. W., and H. Leitner. "Grand canonical Monte Carlo simulation of double-layer capacity." Surface and Coatings Technology 27, no. 1 (January 1986): 29–39. http://dx.doi.org/10.1016/0257-8972(86)90042-3.

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23

Ding, Xue, Fu Yu Liu, and Chao He Yang. "Grand Canonical Monte Carlo Simulations of Olefins Adsorption in Zeolite ZSM-5." Advanced Materials Research 550-553 (July 2012): 321–24. http://dx.doi.org/10.4028/www.scientific.net/amr.550-553.321.

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The adsorption behavior of ethylene and propylene in zeolite ZSM-5 was studied by Grand Canonical Monte Carlo (GCMC) simulations. It is found that ethylene and propylene molecules show different adsorption behaviors in the zeolite cavum. The adsorption isotherms of ethylene and propylene in ZSM-5 at 298K and 823K were simulated. The results exhibit that the molecular adsorption is influenced at various temperatures and pressures, leading to different rules for the adsorption of ethylene and propylene molecules in zeolite. At low temperature, when the pressure is enhanced from 100kpa to 1000 kpa, the adsorption amounts of olefin molecule increase obviously and the loading of ethylene are significantly larger than those of propylene. The adsorption of propylene has a preferential adsorption site in cross position, and nearly reaches saturation at pressure higher than 300kPa. While at 823K the adsorption of ethylene is inhibited with lower loading than those of propylene.

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24

Dornheim, Tobias. "Fermion sign problem in path integral Monte Carlo simulations: grand-canonical ensemble." Journal of Physics A: Mathematical and Theoretical 54, no. 33 (July 28, 2021): 335001. http://dx.doi.org/10.1088/1751-8121/ac1481.

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25

PASSERONE, DANIELE, FURIO ERCOLESSI, FRANCK CELESTINI, and ERIO TOSATTI. "REALISTIC SIMULATIONS OF Au(100): GRAND CANONICAL MONTE CARLO AND MOLECULAR DYNAMICS." Surface Review and Letters 06, no. 05 (October 1999): 663–68. http://dx.doi.org/10.1142/s0218625x99000640.

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The large surface density changes associated with the (100) noble metals surface hex-reconstruction suggest the use of nonparticle-conserving simulation methods. We present an example of a surface Grand Canonical Monte Carlo applied to the transformation of a square nonreconstructed surface to the hexagonally covered low temperature stable Au(100). On the other hand, classical Molecular Dynamics allows one to investigate microscopic details of the reconstruction dynamics, and we show, as an example, retraction of a step and its interplay with the surface reconstruction/deconstruction mechanism.

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26

Cosoli, Paolo, Marco Ferrone, Sabrina Pricl, and Maurizio Fermeglia. "Grand Canonical Monte-Carlo simulations for VOCs adsorption in non-polar zeolites." International Journal of Environmental Technology and Management 7, no. 1/2 (2007): 228. http://dx.doi.org/10.1504/ijetm.2007.013247.

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27

Hudson, T. S., D. Nguyen-Manh, A. C. T. van Duin, and A. P. Sutton. "Grand canonical Monte Carlo simulations of intergranular glassy films in silicon nitride." Materials Science and Engineering: A 422, no. 1-2 (April 2006): 123–35. http://dx.doi.org/10.1016/j.msea.2006.01.014.

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28

Speidel, Joshua A., Jason R. Banfelder, and Mihaly Mezei. "Automatic Control of Solvent Density in Grand Canonical Ensemble Monte Carlo Simulations." Journal of Chemical Theory and Computation 2, no. 5 (July 2006): 1429–34. http://dx.doi.org/10.1021/ct0600363.

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29

Xiong, Jian, Xiang-Jun Liu, Li-Xi Liang, and Qun Zeng. "Investigation of methane adsorption on chlorite by grand canonical Monte Carlo simulations." Petroleum Science 14, no. 1 (January 7, 2017): 37–49. http://dx.doi.org/10.1007/s12182-016-0142-1.

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30

Gruhn, Thomas, and Martin Schoen. "Grand canonical ensemble Monte Carlo simulations of confined `nematic' Gay–Berne films." Thin Solid Films 330, no. 1 (September 1998): 46–58. http://dx.doi.org/10.1016/s0040-6090(98)00799-8.

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31

Song, Mee Kyung, and Kyoung Tai No. "Grand Canonical Monte Carlo simulations of hydrogen adsorption on aluminophosphate molecular sieves." International Journal of Hydrogen Energy 34, no. 5 (March 2009): 2325–28. http://dx.doi.org/10.1016/j.ijhydene.2008.12.076.

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32

Lasich, Matthew, Amir H. Mohammadi, Kim Bolton, Jadran Vrabec, and Deresh Ramjugernath. "Phase equilibria of methane clathrate hydrates from Grand Canonical Monte Carlo simulations." Fluid Phase Equilibria 369 (May 2014): 47–54. http://dx.doi.org/10.1016/j.fluid.2014.02.012.

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33

Herdes, Carmelo, Miguel A. Santos, Francisco Medina, and Lourdes F. Vega. "Precise Characterization of Selected Silica-Based Materials from Grand Canonical Monte Carlo Simulations." Materials Science Forum 514-516 (May 2006): 1396–400. http://dx.doi.org/10.4028/www.scientific.net/msf.514-516.1396.

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We present results concerning the characterization of selected silica-based materials from a molecular modeling approach, together with some physical and mathematical tests to check the reliability of the obtained results. The experimental adsorption data is used in combination with Monte Carlo simulations and a regularization procedure in order to propose a reliable Pore Size Distribution (PSD). Individual adsorption isotherms are obtained by Monte Carlo simulations performed in the Grand Canonical ensemble. The methodology is applied to M41S materials, chosen due to their well defined pore geometry and pore size distribution, obtainable from alternative procedures. Our results are in excellent agreement with previous published results, demonstrating the reliability of this methodology for the characterization of other materials, with less well-defined structural properties.

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34

Papadimitriou, Nikolaos I., Ioannis N. Tsimpanogiannis, Ioannis G. Economou, and Athanassios K. Stubos. "Monte Carlo simulations of the separation of a binary gas mixture (CH4 + CO2) using hydrates." Physical Chemistry Chemical Physics 20, no. 44 (2018): 28026–38. http://dx.doi.org/10.1039/c8cp02050g.

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The current study employs Grand Canonical Monte Carlo simulations in order to calculate the process efficiency of separating CH₄ + CO₂ gas mixtures by utilizing structure sI clathrate hydrates.

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35

Ghoufi, Aziz, Denis Morineau, Ronan Lefort, Ivanne Hureau, Leila Hennous, Haochen Zhu, Anthony Szymczyk, Patrice Malfreyt, and Guillaume Maurin. "Molecular simulations of confined liquids: An alternative to the grand canonical Monte Carlo simulations." Journal of Chemical Physics 134, no. 7 (February 21, 2011): 074104. http://dx.doi.org/10.1063/1.3554641.

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36

Serna, Horacio, Eva G. Noya, and Wojciech T. Góźdź. "The influence of confinement on the structure of colloidal systems with competing interactions." Soft Matter 16, no. 3 (2020): 718–27. http://dx.doi.org/10.1039/c9sm02002k.

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Using grand canonical Monte Carlo simulations, we investigate how the structure of a colloidal fluid with competing interactions can be modified by confinement in channels with different cross-section geometries and sizes.

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37

Schoen, Martin. "Taylor-Expansion Monte Carlo Simulations of Classical Fluids in the Canonical and Grand Canonical Ensemble." Journal of Computational Physics 118, no. 1 (April 1995): 159–71. http://dx.doi.org/10.1006/jcph.1995.1087.

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38

Lee, Hyeonseok, Farnaz A. Shakib, Kouqi Liu, Bo Liu, Bailey Bubach, Rajender S. Varma, Ho Won Jang, Mohammadreza Shokouhimher, and Mehdi Ostadhassan. "Adsorption based realistic molecular model of amorphous kerogen." RSC Advances 10, no. 39 (2020): 23312–20. http://dx.doi.org/10.1039/d0ra04453a.

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This paper reports the results of Grand Canonical Monte Carlo (GCMC)/molecular dynamics (MD) simulations of N₂ and CO₂ gas adsorption on three different organic geomacromolecule (kerogen) models.

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39

Lin, Shiru, Yekun Wang, Yinghe Zhao, Luis R. Pericchi, Arturo J. Hernández-Maldonado, and Zhongfang Chen. "Machine-learning-assisted screening of pure-silica zeolites for effective removal of linear siloxanes and derivatives." Journal of Materials Chemistry A 8, no. 6 (2020): 3228–37. http://dx.doi.org/10.1039/c9ta11909d.

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By a two-step computational process, namely Grand Canonical Monte Carlo (GCMC) simulations and machine learning (ML), we screened 50 959 hypothetical pure-silica zeolites and identified 230 preeminent zeolites with excellent adsorption performances.

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40

Damasceno Borges, Daiane, and Douglas S. Galvao. "Schwarzites for Natural Gas Storage: A Grand-Canonical Monte Carlo Study." MRS Advances 3, no. 1-2 (2018): 115–20. http://dx.doi.org/10.1557/adv.2018.190.

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ABSTRACTThe 3D porous carbon-based structures called Schwarzites have been recently a subject of renewed interest due to the possibility of being synthesized in the near future. These structures exhibit negatively curvature topologies with tuneable porous sizes and shapes, which make them natural candidates for applications such as CO2 capture, gas storage and separation. Nevertheless, the adsorption properties of these materials have not been fully investigated. Following this motivation, we have carried out Grand-Canonical Monte Carlo simulations to study the adsorption of small molecules such as CO2, CO, CH4, N2 and H2, in a series of Schwarzites structures. Here, we present our preliminary results on natural gas adsorptive capacity in association with analyses of the guest-host interaction strengths. Our results show that Schwarzites P7par, P8bal and IWPg are the most promising structures with very high CO2 and CH4 adsorption capacity and low saturation pressure (<1bar) at ambient temperature. The P688 is interesting for H2 storage due to its exceptional high H2 adsorption enthalpy value of -19kJ/mol.

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41

Vlachy, V., and A. D. J. Haymet. "A grand canonical Monte Carlo simulation study of polyelectrolyte solutions." Journal of Chemical Physics 84, no. 10 (May 15, 1986): 5874–80. http://dx.doi.org/10.1063/1.449898.

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42

Vo, Phuong, Hongduo Lu, Ke Ma, Jan Forsman, and Clifford E. Woodward. "Local Grand Canonical Monte Carlo Simulation Method for Confined Fluids." Journal of Chemical Theory and Computation 15, no. 12 (October 30, 2019): 6944–57. http://dx.doi.org/10.1021/acs.jctc.9b00804.

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43

Zara, Stephen J., and David Nicholson. "Grand Canonical Ensemble Monte Carlo Simulation on a Transputer Array." Molecular Simulation 5, no. 3-4 (September 1990): 245–61. http://dx.doi.org/10.1080/08927029008022134.

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44

Baykasoglu, Cengiz, Humeyra Mert, and Celal Utku Deniz. "Grand canonical Monte Carlo simulations of methane adsorption in fullerene pillared graphene nanocomposites." Journal of Molecular Graphics and Modelling 106 (July 2021): 107909. http://dx.doi.org/10.1016/j.jmgm.2021.107909.

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45

Han, K. K., J. H. Cushman, and D. J. Diestler. "Grand canonical Monte Carlo simulations of a Stockmayer fluid in a slit micropore." Molecular Physics 79, no. 3 (June 20, 1993): 537–45. http://dx.doi.org/10.1080/00268979300101431.

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46

Orkoulas, G. "Acceleration of Monte Carlo simulations through spatial updating in the grand canonical ensemble." Journal of Chemical Physics 127, no. 8 (August 28, 2007): 084106. http://dx.doi.org/10.1063/1.2759923.

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47

Luque, Noelia B., Luis Reinaudi, Pablo Serra, and Ezequiel P. M. Leiva. "Electrochemical deposition on surface nanometric defects: Thermodynamics and grand canonical Monte Carlo simulations." Electrochimica Acta 54, no. 11 (April 2009): 3011–19. http://dx.doi.org/10.1016/j.electacta.2008.12.013.

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48

DÖGE, GUNTER, KLAUS MECKE, JESPER MØLLER, DIETRICH STOYAN, and RASMUS P. WAAGEPETERSEN. "GRAND CANONICAL SIMULATIONS OF HARD-DISK SYSTEMS BY SIMULATED TEMPERING." International Journal of Modern Physics C 15, no. 01 (January 2004): 129–47. http://dx.doi.org/10.1142/s0129183104005565.

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The melting transition of hard disks in two dimensions is still an unsolved problem and improved simulation algorithms may be helpful for its investigation. We suggest the application of simulating tempering for grand canonical hard-disk systems as an efficient alternative to the commonly-used Monte Carlo algorithms for canonical systems. This approach allows the direct study of the packing fraction as a function of the chemical potential even in the vicinity of the melting transition. Furthermore, estimates of several spatial characteristics including pair correlation function are studied in order to test the accuracy of the method and to analyze the melting transition in hard-disk systems. Our results seem to show that there is a weak first-order phase transition.

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49

Popov, Maxim N., Thomas Dengg, Dominik Gehringer, and David Holec. "Adsorption of H2 on Penta-Octa-Penta Graphene: Grand Canonical Monte Carlo Study." C — Journal of Carbon Research 6, no. 2 (April 1, 2020): 20. http://dx.doi.org/10.3390/c6020020.

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In this paper, we report the results of hydrogen adsorption properties of a new 2D carbon-based material, consisting of pentagons and octagons (Penta-Octa-Penta-graphene or POP-graphene), based on the Grand-Canonical Monte Carlo simulations. The new material exhibits a moderately higher gravimetric uptake at cryogenic temperatures (77 K), as compared to the regular graphene. We discuss the origin of the enhanced uptake of POP-graphene and offer a consistent explanation.

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50

Ye, Jingyun, Lin Li, and J. Karl Johnson. "The effect of topology in Lewis pair functionalized metal organic frameworks on CO2 adsorption and hydrogenation." Catalysis Science & Technology 8, no. 18 (2018): 4609–17. http://dx.doi.org/10.1039/c8cy01018h.

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We have used density functional theory and classical grand canonical Monte Carlo simulations to identify two functionalized metal organic frameworks (MOFs) that have the potential to be used for both CO₂ capture from flue gas and catalytic conversion of CO₂ to valuable chemicals.

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