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Relevant bibliographies by topics / Molecular Dynamics Simulation Molecular dynamics / Journal articles

Journal articles on the topic 'Molecular Dynamics Simulation Molecular dynamics'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Okumura, Hisashi, Satoru G. Itoh, and Yuko Okamoto. "1P585 Explicit Symplectic Molecular Dynamics Simulation for Rigid-Body Molecules in the Canonical Ensemble(27. Molecular dynamics simulation,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S293. http://dx.doi.org/10.2142/biophys.46.s293_1.

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2

Slavgorodska, Maria, and Alexander Kyrychenko. "Structure and Dynamics of Pyrene-Labeled Poly(acrylic acid): Molecular Dynamics Simulation Study." Chemistry & Chemical Technology 14, no. 1 (February 20, 2020): 76–80. http://dx.doi.org/10.23939/chcht14.01.076.

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3

Gough, Craig A., Takashi Gojobori, and Tadashi Imanishi. "1P563 Consistent dynamic phenomena in amyloidogenic forms of transthyretin : a molecular dynamics study(27. Molecular dynamics simulation,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S287. http://dx.doi.org/10.2142/biophys.46.s287_3.

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4

SHINTO, Hiroyuki. "Molecular Dynamics Simulation." Journal of the Japan Society of Colour Material 86, no. 10 (2013): 380–85. http://dx.doi.org/10.4011/shikizai.86.380.

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5

Krienke, Hartmut. "Molecular dynamics simulation." Journal of Molecular Liquids 75, no. 3 (March 1998): 271–72. http://dx.doi.org/10.1016/s0167-7322(97)00106-2.

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6

Rapaport, D. C. "Molecular dynamics simulation." Computing in Science & Engineering 1, no. 1 (1999): 70–71. http://dx.doi.org/10.1109/5992.743625.

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7

Sugiyama, Ayumu, Tetsunori Yamamoto, Hidemi Nagao, Keigo Nishikawa, Nobutaka Numoto, Kunio Miki, and Yoshihiro Fukumori. "1P567 Molecular dynamics study of dynamical structure stability of giant hemoglobin from Oligobrachia mashikoi(27. Molecular dynamics simulation,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S288. http://dx.doi.org/10.2142/biophys.46.s288_3.

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8

Terada, Tohru, and Kentaro Shimizu. "1P581 Improving efficiency of conformation sampling in multicanonical molecular dynamics simulation(27. Molecular dynamics simulation,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S292. http://dx.doi.org/10.2142/biophys.46.s292_1.

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9

Anam, Muhammad Syaekhul, and S. Suwardi. "Hydration Structures and Dynamics of Ga3+ Ion Based on Molecular Mechanics Molecular Dynamics Simulation (Classical DM)." Indonesian Journal of Chemistry and Environment 4, no. 2 (March 10, 2022): 49–56. http://dx.doi.org/10.21831/ijoce.v4i2.48401.

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The structure and hydration dynamics of Ga3+ ion have been studied using classical Molecular Dynamics (MD) simulations. The data collection procedure includes determining the best base set, constructing 2-body and 3-body potential equations, classical molecular dynamics simulations based on 2-body potentials, classical molecular dynamics simulations based on 2-body + 3 potential-body. The trajectory file data analysis was done to obtain structural properties parameters such as RDF, CND, ADF, and dynamic properties, namely the movement of H2O ligands between hydrations shells. The results of the research indicated that the hydration complex structure of Ga(H2O)83+ and Ga(H2O)63+ was observed in molecular dynamics simulations (MM-2 body) and (MM-2 body + 3-body), respectively. The movement of H2O ligands occurs between the first and second shell or vice versa in the MD simulation of MM-2 bodies but does not occur in MD simulations of (MM-2 bodies + MM-3 bodies). Therefore, the water ligands in the first hydrated shell are stable.

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10

YONEDA, Tomoyuki, Moritaka HIDA, and Akira SAKAKIBARA. "Molecular Dynamics Simulations. Molecular Dynamics Simulation of Ni Crystals under Uniaxial Deformation." Journal of the Society of Materials Science, Japan 46, no. 3 (1997): 228–31. http://dx.doi.org/10.2472/jsms.46.228.

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11

NAKATANI, Keiko, Akihiro NAKATANI, Hiroshi KITAGAWA, and Yoshihiko SUGIYAMA. "Molecular Dynamics Simulations. Molecular Dynamics Simulation on Crack Growth in Amorphous Metal." Journal of the Society of Materials Science, Japan 49, no. 3 (2000): 275–81. http://dx.doi.org/10.2472/jsms.49.275.

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12

Fuchigami, Sotaro, Mitsunori Ikeguchi, and Akinori Kidera. "1P564 All-Atom Molecular Dynamics Simulation of Conformational Changes in Adenylate Kinase(27. Molecular dynamics simulation,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S287. http://dx.doi.org/10.2142/biophys.46.s287_4.

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13

Chen, Xueye. "Molecular dynamics simulation of nanofluidics." Reviews in Chemical Engineering 34, no. 6 (November 27, 2018): 875–85. http://dx.doi.org/10.1515/revce-2016-0060.

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Abstract This review reports the progress on the recent development of molecular dynamics simulation of nanofluidics. Molecular dynamics simulations of nanofluidics in nanochannel structure, surface roughness of nanochannel, carbon nanotubes, electrically charged, thermal transport in nanochannels and gases in nanochannels are illustrated and discussed. This paper will provide an expedient and valuable reference to designers who intend to research molecular dynamics simulation of nanofluidic devices.

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14

Esparza, C. H., and H. Kronmüller. "Canonical molecular dynamics simulation." Molecular Physics 68, no. 6 (December 20, 1989): 1341–52. http://dx.doi.org/10.1080/00268978900102951.

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15

LÜ, Shouqin, and Mian LONG. "Molecular Dynamics Simulation and Molecular Biomechanics." ACTA BIOPHYSICA SINICA 28, no. 1 (2012): 6. http://dx.doi.org/10.3724/sp.j.1260.2012.10150.

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16

Kadupitiya, JCS, Geoffrey C. Fox, and Vikram Jadhao. "Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles." International Journal of High Performance Computing Applications 34, no. 3 (January 14, 2020): 357–74. http://dx.doi.org/10.1177/1094342019899457.

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Simulating the dynamics of ions near polarizable nanoparticles (NPs) using coarse-grained models is extremely challenging due to the need to solve the Poisson equation at every simulation timestep. Recently, a molecular dynamics (MD) method based on a dynamical optimization framework bypassed this obstacle by representing the polarization charge density as virtual dynamic variables and evolving them in parallel with the physical dynamics of ions. We highlight the computational gains accessible with the integration of machine learning (ML) methods for parameter prediction in MD simulations by demonstrating how they were realized in MD simulations of ions near polarizable NPs. An artificial neural network–based regression model was integrated with MD simulation and predicted the optimal simulation timestep and optimization parameters characterizing the virtual system with 94.3% success. The ML-enabled auto-tuning of parameters generated accurate dynamics of ions for ≈ 10 million steps while improving the stability of the simulation by over an order of magnitude. The integration of ML-enhanced framework with hybrid Open Multi-Processing / Message Passing Interface (OpenMP/MPI) parallelization techniques reduced the computational time of simulating systems with thousands of ions and induced charges from thousands of hours to tens of hours, yielding a maximum speedup of ≈ 3 from ML-only acceleration and a maximum speedup of ≈ 600 from the combination of ML and parallel computing methods. Extraction of ionic structure in concentrated electrolytes near oil–water emulsions demonstrates the success of the method. The approach can be generalized to select optimal parameters in other MD applications and energy minimization problems.

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17

Peng, Yan, Su Fen Wang, Yang Zhang, and Ya Nan Gao. "Simulation and Application of Molecular Dynamics in Materials Science." Advanced Materials Research 572 (October 2012): 232–36. http://dx.doi.org/10.4028/www.scientific.net/amr.572.232.

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t was summarized the simulate of materials and application of molecular dynamics, it expounded the molecular dynamics to solve the problem of the basic idea, principle, modeling methods and its simulating methods, and discussed the typical organization performance control technology, the development for simulation aspects and its problems existing. Especially focused on the molecular dynamics system its dynamic simulation in materials microscopic-sized, attached the application of macro characteristics and micro structure. Through the research and analysis, it gave the main application direction in solving steel organization performance control by the method of molecular dynamics, faced with the problem and its future development trend.

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18

Pham, Kien Huu, and Trang Thi Thuy Giap. "The liquid–amorphous phase transition, slow dynamics and dynamical heterogeneity for bulk iron: a molecular dynamics simulation." RSC Advances 11, no. 51 (2021): 32435–45. http://dx.doi.org/10.1039/d1ra06394d.

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19

Khairudin, Nurul Bahiyah Ahmad, and Fatahiya Mohamed Tap. "Molecular Dynamics Folding Simulation of Amyloid A4 Peptide in Implicit Solvent." International Journal of Bioscience, Biochemistry and Bioinformatics 4, no. 5 (2014): 351–54. http://dx.doi.org/10.7763/ijbbb.2014.v4.369.

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20

Hwang, Chi-Chuan, Jee-Gong Chang, Shin-Pon Ju, and Ming-Horng Su. "Nanoscale Impact Dynamics Using Molecular Dynamics Simulation." Journal of the Physical Society of Japan 72, no. 3 (March 15, 2003): 533–44. http://dx.doi.org/10.1143/jpsj.72.533.

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21

Baranyai, András, and Gergely Tóth. "Solvation Dynamics from Nonequilibrium Molecular Dynamics Simulation." Molecular Simulation 14, no. 6 (June 1995): 403–7. http://dx.doi.org/10.1080/08927029508022033.

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22

Dwiastuti, Rini, Muhammad Radifar, Marchaban Marchaban, Sri Noegrohati, and Enade Perdana Istyastono. "Molecular Dynamics Simulations and Empirical Observations on Soy Lecithin Liposome Preparation." Indonesian Journal of Chemistry 16, no. 2 (March 13, 2018): 222. http://dx.doi.org/10.22146/ijc.21167.

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Soy lecithin is a phospholipid often used in liposome formulations. Determination of water and phospholipid composition is one of the problems in the liposome formulation. This study is using molecular dynamics simulation and empirical observation in producing liposome preparations. Phospholipids 1,2-dilauroyl-sn-glycero-3-phosphoethanolamine (DLPE) were objected in molecular dynamics simulations using Coarse Grained Molecular Dynamics (CGMD) approaches. The result showed that the molecular dynamic simulations could be employed to predict the liposome size. The molecular dynamic simulations resulted in liposome size of 71.22 ± 2.54 nm, which was located within the range of the liposome size resulted from the empirical observations (95.99 ± 43.02 nm). Moreover, similar liposome forms were observed on both results of molecular dynamics simulations and empirical approaches.

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23

Miyagawa, Hiroh, and Kunihiro Kitamura. "1P565 Molecular dynamics simulations of association and docking between an inhibitor and an enzyme.(27. Molecular dynamics simulation,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S288. http://dx.doi.org/10.2142/biophys.46.s288_1.

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24

Hirano, Yoshinori, Noriaki Okimoto, Atsushi Suenaga, Makoto Taiji, Naoko Imamoto, Masato Yasui, and Toshikazu Ebisuzaki. "1P590 Investigation of The Structure-Function Relationship of Importin-β by Molecular Dynamics Simulations(27. Molecular dynamics simulation,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S294. http://dx.doi.org/10.2142/biophys.46.s294_2.

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25

Higuchi, Mariko, and Miroslav Pinak. "1P566 Molecular dynamics simulation of clustered DNA damage site with DNA repair enzyme MutM(27. Molecular dynamics simulation,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S288. http://dx.doi.org/10.2142/biophys.46.s288_2.

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26

Chikenji, George. "1P592 All atom molecular dynamics simulations of short peptides for De Novo protein structure prediction(27. Molecular dynamics simulation,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S294. http://dx.doi.org/10.2142/biophys.46.s294_4.

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27

Re, Suyong, Yoshiki Yamaguchi, and Yuji Sugita. "Molecular Dynamics Simulation of Glycans." Trends in Glycoscience and Glycotechnology 32, no. 188 (July 25, 2020): E113—E118. http://dx.doi.org/10.4052/tigg.1616.1e.

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28

Re, Suyong, Yoshiki Yamaguchi, and Yuji Sugita. "Molecular Dynamics Simulation of Glycans." Trends in Glycoscience and Glycotechnology 32, no. 188 (July 25, 2020): J93—J98. http://dx.doi.org/10.4052/tigg.1616.1j.

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29

Haile, J. M., Ian Johnston, A. John Mallinckrodt, and Susan McKay. "Molecular Dynamics Simulation: Elementary Methods." Computers in Physics 7, no. 6 (1993): 625. http://dx.doi.org/10.1063/1.4823234.

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30

van Gunsteren, Wilfred F., and Alan E. Mark. "Validation of molecular dynamics simulation." Journal of Chemical Physics 108, no. 15 (April 15, 1998): 6109–16. http://dx.doi.org/10.1063/1.476021.

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31

Frattini, R., and R. G. Della Valle. "Molecular-dynamics simulation of glassyCu33Y67." Physical Review B 50, no. 6 (August 1, 1994): 3620–24. http://dx.doi.org/10.1103/physrevb.50.3620.

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32

Katsumata, T. "Molecular dynamics simulation in SrTiO3." Solid State Ionics 108, no. 1-4 (May 1, 1998): 175–78. http://dx.doi.org/10.1016/s0167-2738(98)00036-8.

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33

Hollingsworth, Scott A., and Ron O. Dror. "Molecular Dynamics Simulation for All." Neuron 99, no. 6 (September 2018): 1129–43. http://dx.doi.org/10.1016/j.neuron.2018.08.011.

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34

Trumpakaj, Zygmunt, and Bogumił Linde. "Molecular dynamics simulation of pyridine." Journal of Molecular Structure 1085 (April 2015): 268–75. http://dx.doi.org/10.1016/j.molstruc.2014.12.075.

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35

Trumpakaj, Zygmunt, and Bogumił B. J. Linde. "Molecular dynamics simulation of benzene." Journal of Molecular Structure 1107 (March 2016): 231–41. http://dx.doi.org/10.1016/j.molstruc.2015.11.032.

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36

Toxvaerd, S. "Molecular Dynamics Simulation of Prewetting†." Journal of Physical Chemistry C 111, no. 43 (November 2007): 15620–24. http://dx.doi.org/10.1021/jp073665x.

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37

Urbassek, Herbert M. "Molecular-dynamics simulation of sputtering." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 122, no. 3 (February 1997): 427–41. http://dx.doi.org/10.1016/s0168-583x(96)00681-7.

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38

Chaban, Vitaly V., Eudes Eterno Fileti, and Oleg V. Prezhdo. "Buckybomb: Reactive Molecular Dynamics Simulation." Journal of Physical Chemistry Letters 6, no. 5 (February 26, 2015): 913–17. http://dx.doi.org/10.1021/acs.jpclett.5b00120.

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39

Takagi, Ryuzo, Kazutaka Kawamura, and Mitsuhiro Sakawa. "Molecular dynamics simulation of graphite." Journal of Materials Science Letters 6, no. 2 (February 1987): 217–18. http://dx.doi.org/10.1007/bf01728991.

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40

POST, CAROL B., MARTIN KARPLUS, and CHRISTOPHER DOBSON. "A Lysozyme Molecular Dynamics Simulation." Annals of the New York Academy of Sciences 482, no. 1 Computer Simu (December 1986): 267–68. http://dx.doi.org/10.1111/j.1749-6632.1986.tb20960.x.

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41

Bothra, Asim K., Siddhartha Roy, Bhabatarak Bhattacharyya, and Chaitali Mukhopadhyay. "Molecular Dynamics Simulation of Colchicinoids." Journal of Biomolecular Structure and Dynamics 15, no. 5 (April 1998): 999–1008. http://dx.doi.org/10.1080/07391102.1998.10508219.

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42

Liu, Juanfang, Danling Zeng, Qin Li, and Hong Gao. "Molecular dynamics simulation of diffusivity." Frontiers of Energy and Power Engineering in China 2, no. 3 (July 8, 2008): 359–62. http://dx.doi.org/10.1007/s11708-008-0039-9.

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43

Rapaport, D. C. "Molecular dynamics simulation using quaternions." Journal of Computational Physics 60, no. 2 (September 1985): 306–14. http://dx.doi.org/10.1016/0021-9991(85)90009-9.

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44

Pb. "Molecular dynamics simulation, elementary methods." Journal of Molecular Structure: THEOCHEM 288, no. 3 (December 1993): 287–88. http://dx.doi.org/10.1016/0166-1280(93)87060-q.

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45

Fukae, Kazuki, Kazuo Sutoh, and Takuo Yasunaga. "1P575 Potential structure changes of dynein stalk by molecular dynamics calculation(27. Molecular dynamics simulation,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S290. http://dx.doi.org/10.2142/biophys.46.s290_3.

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46

Kikugawa, Gota, Yasushige Yonezawa, Haruki Nakamura, and Ryutaro Himeno. "1P579 Large-scale molecular dynamics simulations with the pairwise electrostatic interaction method for protein-solvent systems(27. Molecular dynamics simulation,Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S291. http://dx.doi.org/10.2142/biophys.46.s291_3.

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47

Mao, B. "Molecular-dynamics investigation of molecular flexibility in ligand binding." Biochemical Journal 288, no. 1 (November 15, 1992): 109–16. http://dx.doi.org/10.1042/bj2880109.

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The molecular flexibility of an inhibitor in ligand-binding process has been investigated by the mass-weighted molecular-dynamics simulation, a computational method adopted from the standard molecular-dynamics simulation and one by which the conformational space of a biomolecular system over potential energy barriers can be sampled effectively. The bimolecular complex of the aspartyl proteinase from Rhizopus chinensis, rhizopuspepsin, and an octapeptide inhibitor was previously studied in a mass-weighted molecular-dynamics simulation; the study has been extended for investigating the molecular flexibility in ligand binding. A series of mass-weighted molecular-dynamics simulations was carried out in which libration of the inhibitor dihedral angles was parametrically controlled, and threshold values of dihedral angle libration amplitudes were observed from monitoring the sampling of the enzyme binding pocket by the inhibitor in the simulations. The computational results are consistent with the general notion of molecular-flexibility requirement for ligand binding; the freedom of dihedral rotations of side-chain groups was found to be particularly important for ligand binding. Thus the critical degree of molecular flexibility which would contribute to effective enzyme inhibition can be obtained precisely from the modified molecular-dynamics simulations; the procedure described herein represents a first step toward providing quantitative measures of such a molecular-flexibility index for inhibitor molecules that have been otherwise targeted for optimal protein-ligand interactions.

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48

Janzen, Alec R., and John W. Leech. "Lattice and molecular dynamics." Canadian Journal of Chemistry 66, no. 4 (April 1, 1988): 852–56. http://dx.doi.org/10.1139/v88-147.

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A frequency analysis is made of a molecular dynamical (MD) simulation of crystalline CS2. At 20 K the peak MD frequencies agree with those calculated using lattice dynamics (LD). However, they are not as dominant as might be expected. At 150 K the peak frequencies are generally not the LD ones. Experience with the Fermi–Pasta–Ulam linear chain problem suggests the reason may be obscuring of fundamental by combination frequencies in highly anharmonic situations.

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49

Ahlstroem, Peter, Olle Teleman, Johan Koerdel, Sture Forsen, and Bo Joensson. "A molecular dynamics simulation of bovine calbindin D9k. Molecular structure and dynamics." Biochemistry 28, no. 8 (April 18, 1989): 3205–11. http://dx.doi.org/10.1021/bi00434a014.

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50

Tran, Anh V., and Yan Wang. "Reliable Molecular Dynamics: Uncertainty quantification using interval analysis in molecular dynamics simulation." Computational Materials Science 127 (February 2017): 141–60. http://dx.doi.org/10.1016/j.commatsci.2016.10.021.

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