A new force field model for the simulation of transport properties of imidazolium-based ionic liquids

2011 ◽  
Vol 13 (17) ◽  
pp. 7910 ◽  
Author(s):  
Vitaly V. Chaban ◽  
Iuliia V. Voroshylova ◽  
Oleg N. Kalugin
Keyword(s):  
Ionic Liquids ◽  
Force Field ◽  
Transport Properties ◽  
Field Model

Development of a coarse-grained force field model of polymeric 1-vinyl-3-ethylimidazolium tetrafluoroborate ionic liquids

2020 ◽  
Vol 754 ◽  
pp. 137656 ◽  
Author(s):  
Qifeng Li ◽  
Yandong Guo ◽  
Jiahuan Tong ◽  
Hongyan He ◽  
Xiaochun Zhang ◽  
...  
Keyword(s):  
Ionic Liquids ◽  
Force Field ◽  
Field Model ◽  
Coarse Grained

Molecular simulations of imidazolium-based tricyanomethanide ionic liquids using an optimized classical force field

2016 ◽  
Vol 18 (9) ◽  
pp. 6850-6860 ◽  
Author(s):  
Niki Vergadou ◽  
Eleni Androulaki ◽  
Jörg-Rüdiger Hill ◽  
Ioannis G. Economou
Keyword(s):  
Ionic Liquids ◽  
Force Field ◽  
Transport Properties ◽  
Molecular Simulations

An atomistic force field is optimized to accurately predict the equilibrium and transport properties of technologically important imidazolium-based tricyanomethanide ionic liquids.

scholarly journals Ionic liquids with anions based on fluorosulfonyl derivatives: from asymmetrical substitutions to a consistent force field model

2017 ◽  
Vol 19 (43) ◽  
pp. 29617-29624 ◽  
Author(s):  
Andreia S. L. Gouveia ◽  
Carlos E. S. Bernardes ◽  
Liliana C. Tomé ◽  
Elena I. Lozinskaya ◽  
Yakov S. Vygodskii ◽  
...  
Keyword(s):  
Ionic Liquids ◽  
Force Field ◽  
Field Model ◽  
Consistent Force Field

Herein, seven anions including four imide-based and two sulfonate anions are considered and compared.

A Transferable, Polarisable Force Field for Ionic Liquids

2019 ◽  
Author(s):  
Kateryna Goloviznina ◽  
José N. Canongia Lopes ◽  
Margarida Costa Gomes ◽  
Agilio Padua
Keyword(s):  
Ionic Liquids ◽  
Force Field ◽  
Force Fields ◽  
Dipole Moments ◽  
Van Der Waals Interactions ◽  
First Principles Calculations ◽  
Ion Diffusion ◽  
Fixed Integer ◽  
Molecular Compounds ◽  
Induced Dipoles

A general, transferable polarisable force field for molecular simulation of ionic liquids and their mixtures with molecular compounds is developed. This polarisable model is derived from the widely used CL\&P fixed-charge force field that describes most families of ionic liquids, in a form compatible with OPLS-AA, one of the major force fields for organic compounds. Models for ionic liquids with fixed, integer ionic charges lead to pathologically slow dynamics, a problem that is corrected when polarisation effects are included explicitly. In the model proposed here, Drude induced dipoles are used with parameters determined from atomic polarisabilities. The CL\&P force field is modified upon inclusion of the Drude dipoles, to avoid double-counting of polarisation effects. This modification is based on first-principles calculations of the dispersion and induction contributions to the van der Waals interactions, using symmetry-adapted perturbation theory (SAPT) for a set of dimers composed of positive, negative and neutral fragments representative of a wide variety of ionic liquids. The fragment approach provides transferability, allowing the representation of a multitude of cation and anion families, including different functional groups, without need to re-parametrise. Because SAPT calculations are expensive an alternative predictive scheme was devised, requiring only molecular properties with a clear physical meaning, namely dipole moments and atomic polarisabilities. The new polarisable force field, CL\&Pol, describes a broad set set of ionic liquids and their mixtures with molecular compounds, and is validated by comparisons with experimental data on density, ion diffusion coefficients and viscosity. The approaches proposed here can also be applied to the conversion of other fixed-charged force fields into polarisable versions.<br>

Tuning force field parameters of ionic liquids using machine learning techniques

2021 ◽  
Vol 200 ◽  
pp. 110759
Author(s):  
Rafikul Islam ◽  
Md Fauzul Kabir ◽  
Saugato Rahman Dhruba ◽  
Khurshida Afroz
Keyword(s):  
Machine Learning ◽  
Ionic Liquids ◽  
Force Field ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Force Field Parameters
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