MD2D: A python module for accurate determination of diffusion coefficient from molecular dynamics
Self-diffusion coefficient can be derived from molecular dynamics (MD) simulations by fitting the mean squared displacement (MSD) into the Einstein relation. However, the finite system size, nonfulfillment of the Brownian motion, and finite simulation time may bring in significant uncertainties that...
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| Veröffentlicht in: | Computer physics communications Jg. 284; S. 108599 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.03.2023
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| ISSN: | 0010-4655, 1879-2944 |
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| Abstract | Self-diffusion coefficient can be derived from molecular dynamics (MD) simulations by fitting the mean squared displacement (MSD) into the Einstein relation. However, the finite system size, nonfulfillment of the Brownian motion, and finite simulation time may bring in significant uncertainties that need to be estimated. We present a python module to facilitate the accurate determination of self-diffusion coefficient from the Einstein relation. We show that the ballistic stage can be clearly recognized and excluded to improve the accuracy and efficiency of self-diffusion coefficient calculation. The correct self-diffusion coefficient and its uncertainty can be conveniently obtained by taking the ensemble average of diffusion coefficients calculated at different time intervals. At the meantime, the module calculates viscosity that can correct the MD-derived self-diffusion coefficient to the thermodynamic limit.
Program Title: MD2D VERSION 1.2.0
CPC Library link to program files:https://doi.org/10.17632/d2x8rw83jb.1
Code Ocean capsule:https://codeocean.com/capsule/2259048
Licensing provisions: GNU General Public License, version 3
Programming language: Python
Nature of problem: Accurate determination of self-diffusion coefficient from molecular dynamics by using the Einstein relation is hampered by the finite system size.
Solution method: Self-diffusion coefficient can be accurately determined from molecular dynamics simulations by fitting the Einstein relation with correct choice of parameters and correcting to the thermodynamic limit. |
|---|---|
| AbstractList | Self-diffusion coefficient can be derived from molecular dynamics (MD) simulations by fitting the mean squared displacement (MSD) into the Einstein relation. However, the finite system size, nonfulfillment of the Brownian motion, and finite simulation time may bring in significant uncertainties that need to be estimated. We present a python module to facilitate the accurate determination of self-diffusion coefficient from the Einstein relation. We show that the ballistic stage can be clearly recognized and excluded to improve the accuracy and efficiency of self-diffusion coefficient calculation. The correct self-diffusion coefficient and its uncertainty can be conveniently obtained by taking the ensemble average of diffusion coefficients calculated at different time intervals. At the meantime, the module calculates viscosity that can correct the MD-derived self-diffusion coefficient to the thermodynamic limit.
Program Title: MD2D VERSION 1.2.0
CPC Library link to program files:https://doi.org/10.17632/d2x8rw83jb.1
Code Ocean capsule:https://codeocean.com/capsule/2259048
Licensing provisions: GNU General Public License, version 3
Programming language: Python
Nature of problem: Accurate determination of self-diffusion coefficient from molecular dynamics by using the Einstein relation is hampered by the finite system size.
Solution method: Self-diffusion coefficient can be accurately determined from molecular dynamics simulations by fitting the Einstein relation with correct choice of parameters and correcting to the thermodynamic limit. |
| ArticleNumber | 108599 |
| Author | Ni, Huaiwei Li, Yunguo |
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| Cites_doi | 10.1103/PhysRevLett.81.5161 10.1080/14786440509463331 10.1002/jcc.21787 10.1021/jp047849c 10.1063/1.1460865 10.1038/s43017-020-0038-x 10.1063/1.465445 10.1021/ac961005a 10.1029/2019GL082722 10.1039/C3CS60199D 10.1016/j.oregeorev.2015.06.018 10.1063/5.0008316 10.1016/j.ces.2010.03.027 10.1130/G37382.1 10.1103/RevModPhys.61.289 10.1021/jp067373s 10.1021/acs.jctc.5b00574 10.1021/acs.jced.9b00495 10.1080/08927022.2020.1810685 10.1103/PhysRevB.59.1758 10.1016/j.fluid.2009.01.001 10.1002/aic.690010222 10.1103/PhysRevB.50.17953 10.1038/s41598-018-22992-5 10.1021/jp047850b 10.1016/j.epsl.2018.11.019 10.1021/jp0477147 10.1021/ed047p261 10.1038/s41524-018-0074-y 10.1007/s10853-017-1978-5 |
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| Title | MD2D: A python module for accurate determination of diffusion coefficient from molecular dynamics |
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