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Journal articles on the topic 'Monte Carlo; Molecules; Ligands'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

Yang, Yang, Charles F. Campana, Gongzhen Cheng, Xinzhan Peng, and Malcolm E. Kenney. "The structure and properties of a sheathed, low reactivity silicon phthalocyanine and the potential for still more inert phthalocyanines." Journal of Porphyrins and Phthalocyanines 18, no. 04 (April 2014): 336–45. http://dx.doi.org/10.1142/s1088424614500060.

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The structure of a silicon phthalocyanine having a sheath composed of eight 1,4-isobutoxy, four bidentate 2,3-dibenzobarreleno substituents, and two trans-heptacyclopentylpentacyclooctasiloxy ligands has been determined by X-ray crystallography. The macrocycle in this compound is nearly completely covered by its sheath, but there is a channel in it which is large enough to give small diatomic molecules easy access to the macrocycle. In solution, transient channels exist in the sheath because of molecular vibrations. The structure of the compound also has been determined by a PM6 semi-empirical calculation. Except for one understandable difference, the results from this calculation are in good agreement with the results from the crystal determination. The van der Waals volume of the molecule has been determined from both the crystal and PM6 data by a Monte Carlo method. The amount of steric hindrance present in analogs of the compound in which its isobutoxy substituents are replaced by other alkoxy groups has been examined through calculations based on van der Waals volumes. Possible analogs of this sheathed molecule are suggested in which the sheath may be impenetrable to even small molecules and thus that are highly resistant to attack.

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2

Sasmal, Sukanya, Samuel C. Gill, Nathan M. Lim, and David L. Mobley. "Sampling Conformational Changes of Bound Ligands Using Nonequilibrium Candidate Monte Carlo and Molecular Dynamics." Journal of Chemical Theory and Computation 16, no. 3 (February 14, 2020): 1854–65. http://dx.doi.org/10.1021/acs.jctc.9b01066.

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3

Freitas, Matheus Puggina de, and Teodorico de Castro Ramalho. "Employing conformational analysis in the molecular modeling of agrochemicals: insights on QSAR parameters of 2,4-D." Ciência e Agrotecnologia 37, no. 6 (December 2013): 485–94. http://dx.doi.org/10.1590/s1413-70542013000600001.

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A common practice to compute ligand conformations of compounds with various degrees of freedom to be used in molecular modeling (QSAR and docking studies) is to perform a conformational distribution based on repeated random sampling, such as Monte-Carlo methods. Further calculations are often required. This short review describes some methods used for conformational analysis and the implications of using selected conformations in QSAR. A case study is developed for 2,4-dichlorophenoxyacetic acid (2,4-D), a widely used herbicide which binds to TIR1 ubiquitin ligase enzyme. The use of such an approach and semi-empirical calculations did not achieve all possible minima for 2,4-D. In addition, the conformations and respective energies obtained by the semi-empirical AM1 method do not match the calculated trends obtained by a high level DFT method. Similar findings were obtained for the carboxylate anion, which is the bioactive form. Finally, the crystal bioactive structure of 2,4-D was not found as a minimum when using Monte-Carlo/AM1 and is similarly populated with another conformer in implicit water solution according to optimization at the B3LYP/aug-cc-pVDZ level. Therefore, quantitative structure-activity relationship (QSAR) methods based on three dimensional chemical structures are not fundamental to provide predictive models for 2,4-D congeners as TIR1 ubiquitin ligase ligands, since they do not necessarily reflect the bioactive conformation of this molecule. This probably extends to other systems.

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4

Hagan, Daniel, and Martin Hagan. "Soft Computing Tools for Virtual Drug Discovery." Journal of Artificial Intelligence and Soft Computing Research 8, no. 3 (July 1, 2018): 173–89. http://dx.doi.org/10.1515/jaiscr-2018-0012.

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AbstractIn this paper, we describe how several soft computing tools can be used to assist in high throughput screening of potential drug candidates. Individual small molecules (ligands) are assessed for their potential to bind to specific proteins (receptors). Committees of multilayer networks are used to classify protein-ligand complexes as good binders or bad binders, based on selected chemical descriptors. The novel aspects of this paper include the use of statistical analyses on the weights of single layer networks to select the appropriate descriptors, the use of Monte Carlo cross-validation to provide confidence measures of network performance (and also to identify problems in the data), the addition of new chemical descriptors to improve network accuracy, and the use of Self Organizing Maps to analyze the performance of the trained network and identify anomalies. We demonstrate the procedures on a large practical data set, and use them to discover a promising characteristic of the data. We also perform virtual screenings with the trained networks on a number of benchmark sets and analyze the results.

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5

Spill, Fabian, Zohar B. Weinstein, Atena Irani Shemirani, Nga Ho, Darash Desai, and Muhammad H. Zaman. "Controlling uncertainty in aptamer selection." Proceedings of the National Academy of Sciences 113, no. 43 (October 7, 2016): 12076–81. http://dx.doi.org/10.1073/pnas.1605086113.

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The search for high-affinity aptamers for targets such as proteins, small molecules, or cancer cells remains a formidable endeavor. Systematic Evolution of Ligands by EXponential Enrichment (SELEX) offers an iterative process to discover these aptamers through evolutionary selection of high-affinity candidates from a highly diverse random pool. This randomness dictates an unknown population distribution of fitness parameters, encoded by the binding affinities, toward SELEX targets. Adding to this uncertainty, repeating SELEX under identical conditions may lead to variable outcomes. These uncertainties pose a challenge when tuning selection pressures to isolate high-affinity ligands. Here, we present a stochastic hybrid model that describes the evolutionary selection of aptamers to explore the impact of these unknowns. To our surprise, we find that even single copies of high-affinity ligands in a pool of billions can strongly influence population dynamics, yet their survival is highly dependent on chance. We perform Monte Carlo simulations to explore the impact of environmental parameters, such as the target concentration, on selection efficiency in SELEX and identify strategies to control these uncertainties to ultimately improve the outcome and speed of this time- and resource-intensive process.

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6

Pranowo, Harno Dwi. "MONTE CARLO SIMULATION OF I^-, Br^-, AND Cl^- IN WATER USING AB INITIO PAIR POTENSIAL FUNCTIONS." Indonesian Journal of Chemistry 7, no. 2 (June 20, 2010): 154–59. http://dx.doi.org/10.22146/ijc.21691.

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Monte Carlo simulations were performed for I-, Br- and Cl-, in water using ab initio pair potential. The systems consisting of one anion in 215 solvent molecules have been simulated at 298 K. Anion-water pair potentials have been newly developed based on ab initio calculations of split valence basis set plus polarization quality. The structure of the solvated ion is discussed in terms of radial distribution functions, coordination number and pair potential distribution. Structural properties were investigated by means of radial distribution functions and their running integration numbers, leading for the first solvation shell to an average 12.60 H2O around I- with I--O distance of 3.74 Å and I--H distance of 2.86 Å, 11.84 H2O around Br- with Br--O distance of 3.40 Å and Br--H distance of 2.42 Å, and 10.68 H2O around Cl- with Cl--O distance of 3.20 Å and Cl--H distance of 2.24 Å, respectively. The structure of the water-anion complexes are agreed with dipole orientation. Pair energy distribution of hydrated anions showed that the pair interaction energy are increase from I-, Cl-, to Br-, namely, -6.28, -9.98 and -13.67 kcal/mol, respectively. The coordination number distribution analysis for the first solvation shell of the all hydrated anions indicated a high exchange rate for the first solvation shell ligands. Keywords: Monte Carlo simulation, halogen anion, ab initio

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7

Palma, Marc, and Yves Louis Pascal. "Étude théorique de la complexation des cations Pb2+ et Hg2+ par le D-talose." Canadian Journal of Chemistry 73, no. 1 (January 1, 1995): 22–40. http://dx.doi.org/10.1139/v95-005.

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Molecular modeling of interactions between D-talopyranose and D-talofuranose conformers and Pb2+ and Hg2+ cations was done in the gaseous state using the AM1 method, both with "naked" species and with some water molecules added. A complete conformational analysis of the free D-talose molecule was carried out using a Monte Carlo method (BATCHMIN program). Lower minima were identified, and the different orientations of the hydroxyl groups given. The calculated equilibrium distribution of anomers in the gaseous phase differs from that obtained experimentally in aqueous solution. This could be related to the predominance of intramolecular hydrogen bonding for the isolated molecule and intermolecular hydrogen bonding in aqueous media. In the presence of a cation, the analysis was carried out using the AM1 method for every conformation of the ligand. In the gaseous phase, Angyal's predictions are not respected. The lowest energy is represented by a flexible β-pyranosic form and some complexes are tetra or pentacoordinated. For the furanosic species, the αF forms bearing a cis-cis-cis sequence of hydroxyl groups are the less stable. Amongst the complexes of carbohydrates with metal cations in aqueous solutions, Pb2+ forms complexes and Hg2+ does not. The calculations show that, for the isolated complexes, the reverse should be true. The lesser stability in water of Hg2+ complexes with respect to Pb2+ could be interpreted in terms of (1) a more unfavourable entropic hydration effect; (2) a less favourable difference of translational entropy. A complete answer requires the dynamical study of the system in water by a Monte Carlo method. Keywords: complexes with divalent cations, D-talopyranose and D-talofuranose conformers.

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8

BUJOTZEK, ALEXANDER, and MARCUS WEBER. "EFFICIENT SIMULATION OF LIGAND–RECEPTOR BINDING PROCESSES USING THE CONFORMATION DYNAMICS APPROACH." Journal of Bioinformatics and Computational Biology 07, no. 05 (October 2009): 811–31. http://dx.doi.org/10.1142/s0219720009004369.

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The understanding of biological ligand–receptor binding processes is relevant for a variety of research topics and assists the rational design of novel drug molecules. Computer simulation can help to advance this understanding, but, due to the high dimensionality of according systems, suffers from the severe computational cost. Based on the framework provided by conformation dynamics and transition state theory, a novel heuristic approach of simulating ligand–receptor binding processes is introduced, which is not dependent on calculating lengthy molecular dynamics trajectories. First, the relevant portion of conformational space is partitioned with meshless methods. Then, each region is sampled separately, using hybrid Monte Carlo. Finally, the dynamical binding process is reconstructed from the static overlaps between the partial densities obtained in the sampling step. The method characterizes the metastable steps of the binding process and can yield the corresponding transition probabilities.

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9

Schmid, Günter, Andreas Lehnert, Ulrich Kreibig, Zbignew Adamczyk, and Peter Belouschek. "Synthese und elektronenmikroskopische Untersuchung kontrolliert gewachsener, ligandstabilisierter Goldkolloide sowie theoretische Überlegungen zur Oberflächenbelegung durch Kolloide / Synthesis and Electron Microscopic Investigation of Controlled Grown, Ligand Stabilized Gold Colloids and Theoretical Considerations on the Covering of Surfaces by Colloids." Zeitschrift für Naturforschung B 45, no. 7 (July 1, 1990): 989–94. http://dx.doi.org/10.1515/znb-1990-0713.

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18 nm Gold colloids are used as seeds for a controlled growth of 36 nm colloids which are then stabilized by P(m-C6H4SO3Na)3. These colloids can be isolated as golden leaflets and are readily soluble in water in virtually any concentration. Electron microscopic investigations prove a very small particle size distribution. X-ray powder diffraction and molecular weight determinations support the results of the electron microscopic investigations. The distance between the colloidal particles in two-dimensional layers corresponds to double-layers of phosphane ligands around each colloid, twice 1.2 nm. Isolated colloids seem to possess thicker ligand shells. A single 44 nm colloid shows a corona of about 7.2 nm thickness, corresponding to 12 phosphane layers. A quantitative description of two-dimensional packing densities is suggested using a novel theoretical model. By means of Monte Carlo simulations the two-dimensional structures are formed during the covering of supports of different geometry can be calculated.

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10

Russo, Anna, Pasqualina Liana Scognamiglio, Rolando Pablo Hong Enriquez, Carlo Santambrogio, Rita Grandori, Daniela Marasco, Antonio Giordano, Giacinto Scoles, and Sara Fortuna. "In Silico Generation of Peptides by Replica Exchange Monte Carlo: Docking-Based Optimization of Maltose-Binding-Protein Ligands." PLOS ONE 10, no. 8 (August 7, 2015): e0133571. http://dx.doi.org/10.1371/journal.pone.0133571.

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11

Wang, Jizeng, and Qiangzeng Huang. "A Stochastic Description on Adhesion of Molecular Bond Clusters Between Rigid Media with Curved Interfaces." International Journal of Applied Mechanics 07, no. 05 (October 2015): 1550071. http://dx.doi.org/10.1142/s1758825115500714.

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Cellular adhesion is mediated by the formation and rupture of specific molecular bonds between ligands and receptors. As an idealized model, based on the Markov process assumption, we present a detailed theoretical analysis of the stochastic dynamics of a cluster of parallel bonds between two rigid bodies with curved interfaces under displacement- and force-controlled loadings, respectively. Regarding the equilibrium cluster size, strength, bond distribution and life time, exact solutions from the corresponding one-step master equations are obtained for the special cases of clusters with fixed separations or clusters with flat interfaces, while the general cases of those with curved interfaces are dealt with numerical techniques. Especially, Monte Carlo simulations are performed to verify the proposed analytical results and to demonstrate interesting differences between stochastic adhesion behaviors of the clusters under loading protocols of fixed separations and fixed forces, respectively.

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12

Manoliu, Laura C. E., Eliza C. Martin, Adina L. Milac, and Laurentiu Spiridon. "Effective Use of Empirical Data for Virtual Screening against APJR GPCR Receptor." Molecules 26, no. 16 (August 12, 2021): 4894. http://dx.doi.org/10.3390/molecules26164894.

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Alzheimer’s disease is a neurodegenerative disorder incompatible with normal daily activity, affecting one in nine people. One of its potential targets is the apelin receptor (APJR), a G-protein coupled receptor, which presents considerably high expression levels in the central nervous system. In silico studies of APJR drug-like molecule binding are in small numbers while high throughput screenings (HTS) are already sufficiently many to devise efficient drug design strategies. This presents itself as an opportunity to optimize different steps in future large scale virtual screening endeavours. Here, we ran a first stage docking simulation against a library of 95 known binders and 3829 generated decoys in an effort to improve the rescoring stage. We then analyzed receptor binding site structure and ligands binding poses to describe their interactions. As a result, we devised a simple and straightforward virtual screening Stage II filtering score based on search space extension followed by a geometric estimation of the ligand—binding site fitness. Having this score, we used an ensemble of receptors generated by Hamiltonian Monte Carlo simulation and reported the results. The improvements shown herein prove that our ensemble docking protocol is suited for APJR and can be easily extrapolated to other GPCRs.

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13

Hong-Xin, Huang, Zhong Zi-Yi, and Cao Ze-Xing. "Variational Monte Carlo Treatment of Molecules." Acta Physico-Chimica Sinica 13, no. 08 (1997): 706–11. http://dx.doi.org/10.3866/pku.whxb19970806.

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14

Nemnyugin, S. A., and A. M. Petrov. "Monte Carlo calculation of muonic molecules." Computer Physics Communications 97, no. 1-2 (August 1996): 175–84. http://dx.doi.org/10.1016/0010-4655(96)00030-6.

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15

KADLEC, PETR, JIŘÍ JANEČEK, and TOMÁŠ BOUBLÍK. "Systems of oblate molecules. Monte Carlo study." Molecular Physics 98, no. 8 (April 20, 2000): 473–79. http://dx.doi.org/10.1080/00268970009483313.

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16

Kadlec, Jiri Janecek, Tomas Boublik, Petr. "Systems of oblate molecules. Monte Carlo study." Molecular Physics 98, no. 8 (April 20, 2000): 473–79. http://dx.doi.org/10.1080/002689700162306.

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17

Galek, Peter T. A., Nicholas C. Handy, and William A. Lester. "Quantum Monte Carlo studies on small molecules." Molecular Physics 104, no. 19 (October 10, 2006): 3069–85. http://dx.doi.org/10.1080/00268970600962071.

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18

Vega, Carlos, and Daan Frenkel. "Monte Carlo study of rod-like molecules." Molecular Physics 67, no. 3 (June 20, 1989): 633–50. http://dx.doi.org/10.1080/00268978900101331.

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19

Zarragoicoechea, G. J., D. Levesque, and J. J. Weis. "Monte Carlo simulations of polar discotic molecules." Molecular Physics 78, no. 6 (April 20, 1993): 1475–92. http://dx.doi.org/10.1080/00268979300100971.

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20

Schmidt, K. E., and J. W. Moskowitz. "Monte Carlo calculations of atoms and molecules." Journal of Statistical Physics 43, no. 5-6 (June 1986): 1027–41. http://dx.doi.org/10.1007/bf02628328.

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21

Phillips, Anthony. "Comparing the dynamics of coordination polyhedra in a metal-cyanide framework." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C590. http://dx.doi.org/10.1107/s2053273314094091.

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The enormous amount of attention devoted to metal-organic framework materials over the past two decades has yielded substantial synthetic control over these materials, so that specific topologies can be explicitly engineered by carefully selecting metal and ligand "building blocks". The dynamic behaviour of these materials, however, remains far less predictable: yet it is this behaviour that is the key to understanding the highly anomalous thermodynamic properties of many framework materials. When studying these compounds crystallographically it is important to acknowledge the limitations of Bragg scattering, which by revealing only a space- and time-averaged structure can give an incomplete or even actively misleading picture of the material's dynamic behaviour. For this reason, complementary techniques, such as XAFS, that are sensitive to the local structure of materials are vitally important. We have studied the behaviour of a prototypical framework material exhibiting near-zero thermal expansion, tetramethylammonium copper(I) zinc(II) cyanide, by X-ray absorption spectroscopy at the Cu and Zn K edges in combination with total neutron and X-ray scattering, supported by reverse Monte Carlo and density-functional theory calculations, thus simultaneously modelling the long-range and local structure of this material. Our results resolve for the first time the individual flexibility of the copper and zinc coordination tetrahedra, suggesting substantially higher flexibility of both types of polyhedra than was previously thought. These results shed new light on the atomic origins of this material's flexibility, and explain the mobility of guest molecules through the framework structure. More generally, this methodology points the way to being able to choose framework components for their dynamic properties, and hence to the rational synthesis of flexible framework materials with targeted thermodynamic properties.

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22

Lester, William A., Lubos Mitas, and Brian Hammond. "Quantum Monte Carlo for atoms, molecules and solids." Chemical Physics Letters 478, no. 1-3 (August 2009): 1–10. http://dx.doi.org/10.1016/j.cplett.2009.06.095.

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23

Stettin, H., T. Schweigert, M. Wahab, and H. J. Mögel. "Monolayers of Multichain Molecules: A Monte Carlo Study." Berichte der Bunsengesellschaft für physikalische Chemie 100, no. 2 (February 1996): 109–13. http://dx.doi.org/10.1002/bbpc.19961000204.

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24

Grimes, R. M., B. L. Hammond, P. J. Reynolds, and W. A. Lester. "Quantum Monte Carlo approach to electronically excited molecules." Journal of Chemical Physics 85, no. 8 (October 15, 1986): 4749–50. http://dx.doi.org/10.1063/1.451754.

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25

Yoshida, Takashi, and Gensho Miyako. "Quantum Monte Carlo with model potentials for molecules." Journal of Chemical Physics 108, no. 19 (May 15, 1998): 8059–61. http://dx.doi.org/10.1063/1.476244.

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26

Schautz, Friedemann, Francesco Buda, and Claudia Filippi. "Excitations in photoactive molecules from quantum Monte Carlo." Journal of Chemical Physics 121, no. 12 (September 22, 2004): 5836–44. http://dx.doi.org/10.1063/1.1777212.

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27

VISHNU KAMATH, P., J. CHANDRASEKHAR, and C. N. R. RAO. "MONTE CARLO SIMULATION OF METHANOL GLASS." Modern Physics Letters B 01, no. 11n12 (February 1988): 395–403. http://dx.doi.org/10.1142/s0217984988001466.

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Monte Carlo simulation studies predict a glass transition in methanol around 180K (experimental Tg, 160K). Methanol exists mainly in the form of linear hydrogen bonded polymers with an average of 10 molecules per polymeric unit in the glass at 180K compared to 6 molecules per unit in the liquid at 298K. There is a considerably greater preponderence of the polymeric. species in the glass relative to the liquid. There is a small change in the distribution of the hydrogen bonded species at Tg, but a significant breaking down of the polymeric species occurs above 220K. The average coordination number as well as the gmin/gmax ratios show a change around 220K instead of at Tg. The simulated distribution of hydrogen bonded species is comparable to the results obtained from infra-red spectroscopy.

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28

Caprion, D. "Discotic molecules in cylindrical nanopores: A Monte Carlo study." European Physical Journal E 28, no. 3 (February 3, 2009): 305–13. http://dx.doi.org/10.1140/epje/i2008-10412-6.

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29

Cameron, Brett R., and Peter W. Harland. "Monte Carlo calculation of rotational relaxation in small molecules." Journal of the Chemical Society, Faraday Transactions 89, no. 19 (1993): 3517. http://dx.doi.org/10.1039/ft9938903517.

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30

Stettin, H., R. Friedemann, and H. J. Mögel. "Monte Carlo Simulations of Supported Monolayers of Amphiphilic Molecules." Berichte der Bunsengesellschaft für physikalische Chemie 97, no. 1 (January 1993): 44–48. http://dx.doi.org/10.1002/bbpc.19930970109.

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31

Vacatello, Michele, and Mariacristina Iovino. "Monte Carlo simulations of a liquid of mesogenic molecules." Journal of Chemical Physics 104, no. 7 (February 15, 1996): 2721–24. http://dx.doi.org/10.1063/1.470993.

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32

Koga, Yoshio, Yoshio Iwai, and Yasuhiko Arai. "Monte Carlo simulation for chain molecules in supercritical ethane." Journal of Chemical Physics 101, no. 3 (August 1994): 2283–88. http://dx.doi.org/10.1063/1.468433.

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33

van Giessen, A. E., and I. Szleifer. "Monte Carlo simulations of chain molecules in confined environments." Journal of Chemical Physics 102, no. 22 (June 8, 1995): 9069–76. http://dx.doi.org/10.1063/1.468855.

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34

Burde, Jared T., and M. Mercedes Calbi. "Adsorption kinetics of diatomic molecules." Phys. Chem. Chem. Phys. 16, no. 17 (2014): 8070–77. http://dx.doi.org/10.1039/c3cp55458a.

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Wells, Stephen A., Naomi F. Cessford, Nigel A. Seaton, and Tina Düren. "Early stages of phase selection in MOF formation observed in molecular Monte Carlo simulations." RSC Advances 9, no. 25 (2019): 14382–90. http://dx.doi.org/10.1039/c9ra01504c.

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36

Mladenovic, Marko, and Igor Stankovic. "Monte Carlo simulations of crystalline organic semiconductors." Serbian Journal of Electrical Engineering 10, no. 1 (2013): 125–34. http://dx.doi.org/10.2298/sjee1301125m.

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Molecular model for crystalline organic semiconductors based on small molecules is implemented in three-dimensional Monte Carlo simulations. In this paper results for naphthalene are presented. Molecular structure is considered in two configurations: within a single monocrystal and in vicinity of interface between two monocrystals with different crystalline orientations.

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Cole, Daniel J., Julian Tirado-Rives, and William L. Jorgensen. "Molecular dynamics and Monte Carlo simulations for protein–ligand binding and inhibitor design." Biochimica et Biophysica Acta (BBA) - General Subjects 1850, no. 5 (May 2015): 966–71. http://dx.doi.org/10.1016/j.bbagen.2014.08.018.

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38

Tyagi, Pawan, Christopher D'Angelo, and Collin Baker. "Monte Carlo and Experimental Magnetic Studies of Molecular Spintronics Devices." Nano 10, no. 04 (June 2015): 1550056. http://dx.doi.org/10.1142/s1793292015500563.

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Molecule-based spintronics devices (MSDs) are highly promising candidates for discovering advanced logic and memory computer units. An advanced MSD will require the placement of paramagnetic molecules between the two ferromagnetic (FM) electrodes. Due to extreme fabrication challenges, only a couple of experimental studies could be performed to understand the effect of magnetic molecules on the overall magnetic and transport properties of MSDs. To date, theoretical studies mainly focused on charge and spin transport aspects of MSDs; there is a dearth of knowledge about the effect of magnetic molecules on the magnetic properties of MSDs. This paper investigates the effect of magnetic molecules, with a net spin, on the magnetic properties of 2D MSDs via Monte Carlo (MC) simulations. Our MC simulations encompass a wide range of MSDs that can be realized by establishing different kinds of magnetic interactions between molecules and FM electrodes at different temperatures. The MC simulations show that ambient thermal energy strongly influenced the molecular coupling effect on the MSD. We studied the nature and strength of molecule couplings (FM and antiferromagnetic) with the two electrodes on the magnetization, specific heat and magnetic susceptibility of MSDs. For the case when the nature of molecule interaction was FM with one electrode and antiferromagnetic with another electrode the overall magnetization shifted toward zero. In this case, the effect of molecules was also a strong function of the nature and strength of direct coupling between FM electrodes. In the case when molecules make opposite magnetic couplings with the two FM electrodes, the MSD model used for MC studies resembled with the magnetic tunnel junction based MSD. The experimental magnetic studies on these devices are in agreement with our theoretical MC simulations results. Our MC simulations will enable the fundamental understanding and designing of a wide range of novel spintronics devices utilizing a variety of molecules, nanoclusters and quantum dots as the device elements.

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39

Kim, Jihan, and Berend Smit. "Efficient Monte Carlo Simulations of Gas Molecules Inside Porous Materials." Journal of Chemical Theory and Computation 8, no. 7 (June 28, 2012): 2336–43. http://dx.doi.org/10.1021/ct3003699.

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40

Brown, Willard R., William A. Glauser, and William A. Lester. "Quantum Monte Carlo for floppy molecules: Vibrational states of C3." Journal of Chemical Physics 103, no. 22 (December 8, 1995): 9721–25. http://dx.doi.org/10.1063/1.469935.

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41

MATSUMOTO, Hiroaki, and Daisuke KUDO. "Monte Carlo Simulation of Diatomic Molecules using the Flexible Model." Transactions of the Japan Society of Mechanical Engineers Series B 68, no. 669 (2002): 1337–43. http://dx.doi.org/10.1299/kikaib.68.1337.

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42

Harismiadis, Vassilis I., and Igal Szleifer. "Second virial coefficients of chain molecules: A Monte Carlo study." Molecular Physics 81, no. 4 (March 1994): 851–66. http://dx.doi.org/10.1080/00268979400100571.

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43

Müller, Erich A., Lourdes F. Vega, Keith E. Gubbins, and Luis F. Rull. "Adsorption isotherms of associating chain molecules from Monte Carlo simulations." Molecular Physics 85, no. 1 (May 1995): 9–21. http://dx.doi.org/10.1080/00268979500100901.

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Zhurkin, D. V., and A. L. Rabinovich. "Monte Carlo properties of the hydrocarbon chains of phospholipid molecules." Russian Journal of Physical Chemistry A 89, no. 2 (December 30, 2014): 242–49. http://dx.doi.org/10.1134/s0036024415020387.

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Sun, Zhiwei, Peter J. Reynolds, R. Kent Owen, and William A. Lester. "Monte Carlo study of electron correlation functions for small molecules." Theoretica Chimica Acta 75, no. 5 (1989): 353–68. http://dx.doi.org/10.1007/bf00526694.

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Hammond, Brian L., Maria M. Soto, Robert N. Barnett, and William A. Lester. "On quantum Monte Carlo for the electronic structure of molecules." Journal of Molecular Structure: THEOCHEM 234 (September 1991): 525–38. http://dx.doi.org/10.1016/0166-1280(91)89033-w.

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Mitas, Lubos. "Electronic structure by quantum Monte Carlo: atoms, molecules and solids." Computer Physics Communications 96, no. 2-3 (August 1996): 107–17. http://dx.doi.org/10.1016/0010-4655(96)00063-x.

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Freire, Juan J., Ana M. Rubio, and Antonio Poncela. "Monte carlo calculations of second virial coefficients of chain molecules." Macromolecular Symposia 121, no. 1 (August 1997): 97–110. http://dx.doi.org/10.1002/masy.19971210110.

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Abalmasov, Veniamin A. "Dipole ordering of water molecules in cordierite: Monte Carlo simulations." Journal of Physics: Condensed Matter 33, no. 34 (June 25, 2021): 34LT01. http://dx.doi.org/10.1088/1361-648x/ac06f0.

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Gehlhaar, Daniel K., Karl E. Moerder, Dominic Zichi, Christopher J. Sherman, Richard C. Ogden, and Stephan T. Freer. "De Novo Design of Enzyme Inhibitors by Monte Carlo Ligand Generation." Journal of Medicinal Chemistry 38, no. 3 (February 1995): 466–72. http://dx.doi.org/10.1021/jm00003a010.

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