Key Points
Force Fields and Interactions |
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Fast Methods to Evaluate Forces |
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Advancing Simulation in Time |
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Periodic Boundary Conditions |
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Degrees of Freedom |
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Electrostatic Interactions |
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Controlling Temperature |
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Controlling Pressure |
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Water models |
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Supplemental: Overview of the Common Force Fields |
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Text Books and Additional Resources
Here is a selection of textbooks and other resources that cover molecular dynamics.
While the following resources are listed in no particular order, special consideration should be given to the Living Journal of Computational Molecular Science, which publishes peer-reviewed articles in categories like “Best Practices”, “Tutorials”, “Lessons Learned” as Open Access and aims to updating regularly. Topics range from general Best Practices in Molecular Simulations to more specialized topics like Best Practices for Computing Transport Properties, Best Practices for Quantification of Sampling Quality in Molecular Simulations, Simulation Best Practices for Lipid Membranes, and Lessons Learned from the Calculation of One-Dimensional Potentials of Mean Force.
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David L. Mobley, Michael R. Shirts, Daniel M. Zuckerman (Managing Editors).
Living Journal of Computational Molecular Science (LiveCoMS)
(Peer-reviewed manuscripts which share best practices in molecular modeling and simulation.)
Open Access articles available at: https://livecomsjournal.org/ -
Michael P. Allen and Dominic J. Tildesley.
Computer Simulation of Liquids. Second Edition
Oxford University Press; 2017. ISBN: 9780198803195
doi:10.1093/oso/9780198803195.001.0001 -
Daan Frenkel and Berend Smit.
Understanding Molecular Simulation: From Algorithms to Applications
Academic Press; 2nd edition; 2001. ISBN hardcover: 9780122673511 ISBN eBook: 9780080519982
doi:10.1016/B978-0-12-267351-1.X5000-7 -
Dennis C. Rapaport.
The Art of Molecular Dynamics Simulation 2nd Edition
Cambridge University Press; 2004. ISBN: 9780521825689
doi:10.1017/CBO9780511816581 -
Thijs J.H. Vlugt, Jan P.J.M. van der Eerden, Marjolein Dijkstra, Berend Smit, Daan Frenkel
Introduction to Molecular Simulation and Statistical Thermodynamics
Delft, The Netherlands, 2008. ISBN: 978-90-9024432-7
Free PDF available from: http://homepage.tudelft.nl/v9k6y/imsst/
Glossary
FIXME
- barostat
- Pressure control algorithms in molecular dynamics (MD) simulations are commonly referred to as barostats and are needed to study isobaric systems. Barostats work by altering the size of the simulation box. Therefore they can only be used in conjunction with periodic boundary conditions (PBC).
- ergodicity
- In statistical mechanics ergodicity describes the principle that studying a single particle averaged over a long time is equivalent to averaging over many particles which are studied for a short time. See also: “Ergodicity” on Wikipedia
- periodic boundary conditions
- Periodic boundary conditions (PBC) … See also:
- thermostat
- Temperature control algorithms in molecular dynamics (MD) simulations are commonly referred to as thermostats and are needed to study isothermal systems. Thermostats work by altering the velocities of particles.
Bibliography
- Ahmed-2010
- Ahmed A, Sadus RJ.
Effect of potential truncations and shifts on the solid-liquid phase coexistence of Lennard-Jones fluids.
J Chem Phys. 2010;133: 124515. doi:10.1063/1.3481102 - Allen-2017
- Allen MP, Tildesley DJ.
Computer Simulation of Liquids. Second Edition
Oxford University Press; 2017. ISBN: 9780198803195 doi:10.1093/oso/9780198803195.001.0001 - Andersen-1980
- Andersen HC.
Molecular dynamics simulations at constant pressure and/or temperature.
J Chem Phys. 1980;72: 2384–2393. doi:10.1063/1.439486 - Basconi-2013
- Basconi JE, Shirts MR.
Effects of Temperature Control Algorithms on Transport Properties and Kinetics in Molecular Dynamics Simulations.
J Chem Theory Comput. 2013;9: 2887–2899. doi:10.1021/ct400109a - Berendsen-1984
- Berendsen HJC, Postma JPM, van Gunsteren WF, DiNola A, Haak JR.
Molecular dynamics with coupling to an external bath.
J Chem Phys. $abstract.copyright_name.value; 1984;81: 3684–90. doi:10.1063/1.448118 - Bernetti-2020
- Bernetti M, Bussi G
Pressure control using stochastic cell rescaling.
J Chem Phys. 2020;153:114107 doi:10.1063/5.0020514 - Braun-2019
- Braun E, Gilmer J, Mayes HB, Mobley DL, Monroe JI, Prasad S, et al.
Best Practices for Foundations in Molecular Simulations [Article v1.0].
Living J Comput Mol Sci. 2019;1: 1–28. doi:10.33011/livecoms.1.1.5957 - Bussi-2007
- Bussi G, Donadio D, Parrinello M.
Canonical sampling through velocity rescaling.
J Chem Phys. 2007;126: 014101. doi:10.1063/1.2408420 - Dauber-Osguthorpe-2018
- Dauber-Osguthorpe P, Hagler AT.
Biomolecular force fields: where have we been, where are we now, where do we need to go and how do we get there?
Journal of Computer-Aided Molecular Design. 2019;33: 133–203. doi:10.1007/s10822-018-0111-4 - Grosfils-2009
- Grosfils P, Lutsko JF.
Dependence of the liquid-vapor surface tension on the range of interaction: a test of the law of corresponding states.
J Chem Phys. 2009;130: 054703. doi:10.1063/1.3072156 - Grossfield-2019
- Grossfield A, Patrone PN, Roe DR, Schultz AJ, Siderius D, Zuckerman DM.
Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0].
Living J Comput Mol Sci. 2019;1: 1–24. doi:10.33011/livecoms.1.1.5067 - Hagler-2019
- Hagler AT.
Force field development phase II: Relaxation of physics-based criteria… or inclusion of more rigorous physics into the representation of molecular energetics.
J Comput Aided Mol Des. 2019;33: 205–264. doi:10.1007/s10822-018-0134-x - Hoover-1985
- Hoover WG.
Canonical dynamics: Equilibrium phase-space distributions.
Phys Rev A. APS; 1985;31: 1695–1697. doi:10.1103/PhysRevA.31.1695 - Huang-2014
- Huang K, García AE.
Effects of truncating van der Waals interactions in lipid bilayer simulations.
Journal of Chemical Physics. 2014;141. doi:10.1063/1.4893965 - Koopman-2006
- Koopman EA, Lowe CP.
Advantages of a Lowe-Andersen thermostat in molecular dynamics simulations.
J Chem Phys. 2006;124: 1–6. doi:10.1063/1.2198824 - Larsson-2011
- Larsson P, Hess B, Lindahl E.
Algorithm improvements for molecular dynamics simulations.
Wiley Interdiscip Rev Comput Mol Sci. 2011;1: 93–108. doi:10.1002/wcms.3 - Lemkul-2019
- Lemkul J.
From Proteins to Perturbed Hamiltonians: A Suite of Tutorials for the GROMACS-2018 Molecular Simulation Package [Article v1.0].
Living J Comput Mol Sci. 2019;1: 1–53. doi:10.33011/livecoms.1.1.5068 - Lifson-1968
- Lifson S, Warshel A.
Consistent Force Field for Calculations of Conformations, Vibrational Spectra, and Enthalpies of Cycloalkane and n‐Alkane Molecules.
J Chem Phys. 1968;49: 5116–5129. doi:10.1063/1.1670007 - Maginn-2020
- Maginn EJ, Messerly RA, Carlson DJ, Roe DR, Elliot JR.
Best Practices for Computing Transport Properties 1. Self-Diffusivity and Viscosity from Equilibrium Molecular Dynamics [Article v1.0].
Living J Comput Mol Sci. 2020;2: 1–20. doi:10.33011/livecoms.1.1.6324 - Markthaler-2019
- Markthaler D, Jakobtorweihen S, Hansen N.
Lessons Learned from the Calculation of One-Dimensional Potentials of Mean Force [Article v1.0].
Living J Comput Mol Sci. 2019;1: 1–25. doi:10.33011/livecoms.1.2.11073 - Martyna-1992
- Martyna GJ, Klein ML, Tuckerman M.
Nosé–Hoover chains: The canonical ensemble via continuous dynamics.
J Chem Phys. 1992;97: 2635–2643. doi:10.1063/1.463940 - Martyna-1996
- Martyna GJ, Tuckerman ME, Tobias DJ, Klein ML
Explicit reversible integrators for extended systems dynamics.
Mol Phys. 1996;87(5): 1117-1157. doi:10.1080/00268979600100761 - Nose-1984
- Nosé S.
A molecular dynamics method for simulations in the canonical ensemble.
Mol Phys. 1984;52: 255–268. doi:10.1080/00268978400101201 - Parrinello-1980
- Parrinello, M, Rahman, A
Crystal Structure and Pair Potentials: A Molecular-Dynamics Study
Phys. Rev. Lett. 1980; 45, 1196. doi:10.1103/PhysRevLett.45.1196 - Parrinello-1981
- Parrinello, M, Rahman, A
Polymorphic transitions in single crystals: A new molecular dynamics method.
J Appl Phys. 1981; 52(12):7182. doi:10.1063/1.328693 - Piana-2012
- Piana S, Lindorff-Larsen K, Dirks RM, Salmon JK, Dror RO, Shaw DE.
Evaluating the Effects of Cutoffs and Treatment of Long-range Electrostatics in Protein Folding Simulations.
PLOS ONE. 2012;7: e39918. doi:10.1371/journal.pone.0039918 - Rogge-2015
- Rogge SMJ, Vanduyfhuys L, Ghysels A, Waroquier M, Verstraelen T, Maurin G, Van Speybroeck V.
A Comparison of Barostats for the Mechanical Characterization of Metal–Organic Frameworks
J Chem Theory Comput. 2015;11: 5583-97. doi:10.1021/acs.jctc.5b00748 - Shirts-2013
- Shirts MR.
Simple Quantitative Tests to Validate Sampling from Thermodynamic Ensembles.
J Chem Theory Comput. 2013;9: 909–926. doi:10.1021/ct300688p - Smith-2019
- Smith DJ, Klauda JB, Sodt AJ.
Simulation Best Practices for Lipid Membranes [Article v1.0].
Living J Comput Mol Sci. 2019;1: 1–31. doi:10.33011/livecoms.1.1.5966 - Verlet-1967
- Verlet L. Computer “Experiments” on Classical Fluids. I.
Thermodynamical Properties of Lennard-Jones Molecules.
Phys Rev. 1967;159: 98–103. doi:10.1103/PhysRev.159.98 - Winger-2009
- Winger M, Trzesniak D, Baron R, van Gunsteren WF.
On using a too large integration time step in molecular dynamics simulations of coarse-grained molecular models.
Phys Chem Chem Phys. 2009;11: 1934–1941. doi:10.1039/b818713d - Wong-Ekkabut-2016
- Wong-Ekkabut J, Karttunen M.
The good, the bad and the user in soft matter simulations.
Biochim Biophys Acta - Biomembr. Elsevier B.V.; 2016;1858: 2529–2538. doi:10.1016/j.bbamem.2016.02.004 - Yonetani-2006
- Yonetani Y.
Liquid water simulation: A critical examination of cutoff length.
Journal of Chemical Physics. 2006;124. doi:10.1063/1.2198208