skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Screening for High Conductivity/Low Viscosity Ionic Liquids Using Product Descriptors

Abstract

We seek to optimize Ionic liquids (ILs) for application to redox flow batteries. As part of this effort, we have developed a computational method for suggesting ILs with high conductivity and low viscosity. Since ILs consist of cation-anion pairs, we consider a method for treating ILs as pairs using product descriptors for QSPRs, a concept borrowed from the prediction of protein-protein interactions in bioinformatics. We demonstrate the method by predicting electrical conductivity, viscosity, and melting point on a dataset taken from the ILThermo database on June 18th, 2014. The dataset consists of 4,329 measurements taken from 165 ILs made up of 72 cations and 34 anions. In conclusion, we benchmark our QSPRs on the known values in the dataset then extend our predictions to screen all 2,448 possible cation-anion pairs in the dataset.

Authors:
 [1];  [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1375027
Report Number(s):
SAND-2016-3631J
Journal ID: ISSN 1868-1743; 638469
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
Molecular Informatics
Additional Journal Information:
Journal Volume: 36; Journal Issue: 7; Journal ID: ISSN 1868-1743
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Martin, Shawn, Pratt, III, Harry D., and Anderson, Travis M. Screening for High Conductivity/Low Viscosity Ionic Liquids Using Product Descriptors. United States: N. p., 2017. Web. doi:10.1002/minf.201600125.
Martin, Shawn, Pratt, III, Harry D., & Anderson, Travis M. Screening for High Conductivity/Low Viscosity Ionic Liquids Using Product Descriptors. United States. doi:10.1002/minf.201600125.
Martin, Shawn, Pratt, III, Harry D., and Anderson, Travis M. Tue . "Screening for High Conductivity/Low Viscosity Ionic Liquids Using Product Descriptors". United States. doi:10.1002/minf.201600125. https://www.osti.gov/servlets/purl/1375027.
@article{osti_1375027,
title = {Screening for High Conductivity/Low Viscosity Ionic Liquids Using Product Descriptors},
author = {Martin, Shawn and Pratt, III, Harry D. and Anderson, Travis M.},
abstractNote = {We seek to optimize Ionic liquids (ILs) for application to redox flow batteries. As part of this effort, we have developed a computational method for suggesting ILs with high conductivity and low viscosity. Since ILs consist of cation-anion pairs, we consider a method for treating ILs as pairs using product descriptors for QSPRs, a concept borrowed from the prediction of protein-protein interactions in bioinformatics. We demonstrate the method by predicting electrical conductivity, viscosity, and melting point on a dataset taken from the ILThermo database on June 18th, 2014. The dataset consists of 4,329 measurements taken from 165 ILs made up of 72 cations and 34 anions. In conclusion, we benchmark our QSPRs on the known values in the dataset then extend our predictions to screen all 2,448 possible cation-anion pairs in the dataset.},
doi = {10.1002/minf.201600125},
journal = {Molecular Informatics},
number = 7,
volume = 36,
place = {United States},
year = {2017},
month = {2}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 3 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Ionic liquids: perspectives for organic and catalytic reactions
journal, May 2002

  • Olivier-Bourbigou, Hélène; Magna, Lionel
  • Journal of Molecular Catalysis A: Chemical, Vol. 182-183, p. 419-437
  • DOI: 10.1016/S1381-1169(01)00465-4

Recent developments of task-specific ionic liquids in organic synthesis
journal, March 2011


Quantitative Structure−Property Relationships for Melting Points and Densities of Ionic Liquids
journal, January 2005

  • Trohalaki, Steven; Pachter, Ruth; Drake, Greg W.
  • Energy & Fuels, Vol. 19, Issue 1
  • DOI: 10.1021/ef049858q

Ionic Liquids for Clean Technology
journal, April 1997


Ionic liquids. Green solvents for the future
journal, January 2000


Preparation of Inorganic Materials Using Ionic Liquids
journal, January 2010


Development of a LSSVM-GC model for estimating the electrical conductivity of ionic liquids
journal, January 2014

  • Gharagheizi, Farhad; Ilani-Kashkouli, Poorandokht; Sattari, Mehdi
  • Chemical Engineering Research and Design, Vol. 92, Issue 1
  • DOI: 10.1016/j.cherd.2013.06.015

Predictive methods for the estimation of thermophysical properties of ionic liquids
journal, January 2012

  • Coutinho, João A. P.; Carvalho, Pedro J.; Oliveira, Nuno M. C.
  • RSC Advances, Vol. 2, Issue 19
  • DOI: 10.1039/c2ra20141k

Predictive Quantitative Structure–Property Relationship Model for the Estimation of Ionic Liquid Viscosity
journal, January 2012

  • Mirkhani, Seyyed Alireza; Gharagheizi, Farhad
  • Industrial & Engineering Chemistry Research, Vol. 51, Issue 5
  • DOI: 10.1021/ie2025823

Computer-aided reverse design for ionic liquids by QSPR using descriptors of group contribution type for ionic conductivities and viscosities
journal, December 2007

  • Matsuda, Hiroyuki; Yamamoto, Hiroshi; Kurihara, Kiyofumi
  • Fluid Phase Equilibria, Vol. 261, Issue 1-2
  • DOI: 10.1016/j.fluid.2007.07.018

Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR
journal, November 2004

  • Sheridan, Robert P.; Feuston, Bradley P.; Maiorov, Vladimir N.
  • Journal of Chemical Information and Computer Sciences, Vol. 44, Issue 6
  • DOI: 10.1021/ci049782w

Room-Temperature Ionic Liquids. Solvents for Synthesis and Catalysis
journal, August 1999

  • Welton, Thomas
  • Chemical Reviews, Vol. 99, Issue 8, p. 2071-2084
  • DOI: 10.1021/cr980032t

Predicting protein-protein interactions using signature products
journal, August 2004


Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties
journal, January 2012

  • Le, Tu; Epa, V. Chandana; Burden, Frank R.
  • Chemical Reviews, Vol. 112, Issue 5
  • DOI: 10.1021/cr200066h

In Silico Design of New Ionic Liquids Based on Quantitative Structure−Property Relationship Models of Ionic Liquid Viscosity
journal, January 2011

  • Billard, I.; Marcou, G.; Ouadi, A.
  • The Journal of Physical Chemistry B, Vol. 115, Issue 1
  • DOI: 10.1021/jp107868w

Anion and Cation Effects on Imidazolium Salt Melting Points: A Descriptor Modelling Study
journal, April 2007

  • López-Martin, Ignacio; Burello, Enrico; Davey, Paul N.
  • ChemPhysChem, Vol. 8, Issue 5
  • DOI: 10.1002/cphc.200600637

Understanding chemical reaction mechanisms in ionic liquids: successes and challenges
journal, January 2011

  • Hubbard, Colin D.; Illner, Peter; van Eldik, Rudi
  • Chem. Soc. Rev., Vol. 40, Issue 1
  • DOI: 10.1039/C0CS00043D

Advances in QSPR/QSTR models of ionic liquids for the design of greener solvents of the future
journal, January 2013


Comparative Studies on Some Metrics for External Validation of QSPR Models
journal, January 2012

  • Roy, Kunal; Mitra, Indrani; Kar, Supratik
  • Journal of Chemical Information and Modeling, Vol. 52, Issue 2
  • DOI: 10.1021/ci200520g

Electrochemical Reactivity in Room-Temperature Ionic Liquids
journal, July 2008

  • Hapiot, Philippe; Lagrost, Corinne
  • Chemical Reviews, Vol. 108, Issue 7
  • DOI: 10.1021/cr0680686

Exhaustive QSPR Studies of a Large Diverse Set of Ionic Liquids:  How Accurately Can We Predict Melting Points?
journal, March 2007

  • Varnek, Alexandre; Kireeva, Natalia; Tetko, Igor V.
  • Journal of Chemical Information and Modeling, Vol. 47, Issue 3
  • DOI: 10.1021/ci600493x

Application of Redox Non-Innocent Ligands to Non-Aqueous Flow Battery Electrolytes
journal, September 2013

  • Cappillino, Patrick J.; Pratt, Harry D.; Hudak, Nicholas S.
  • Advanced Energy Materials, Vol. 4, Issue 1
  • DOI: 10.1002/aenm.201300566

Data and QSPR study for viscosity of imidazolium-based ionic liquids
journal, January 2011


Viscosity of ionic liquids: Database, observation, and quantitative structure-property relationship analysis
journal, October 2011

  • Yu, Guangren; Zhao, Dachuan; Wen, Lu
  • AIChE Journal, Vol. 58, Issue 9
  • DOI: 10.1002/aic.12786

Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction
journal, October 2010

  • Katritzky, Alan R.; Kuanar, Minati; Slavov, Svetoslav
  • Chemical Reviews, Vol. 110, Issue 10
  • DOI: 10.1021/cr900238d

Synthesis and characterization of ionic liquids containing copper, manganese, or zinc coordination cations
journal, January 2011

  • Pratt III, Harry D.; Rose, Alyssa J.; Staiger, Chad L.
  • Dalton Transactions, Vol. 40, Issue 43
  • DOI: 10.1039/c1dt10973a

Some case studies on application of “ r m 2 ” metrics for judging quality of quantitative structure-activity relationship predictions: Emphasis on scaling of response data
journal, January 2013

  • Roy, Kunal; Chakraborty, Pratim; Mitra, Indrani
  • Journal of Computational Chemistry, Vol. 34, Issue 12
  • DOI: 10.1002/jcc.23231

Estimation of Ionic Conductivity and Viscosity of Ionic Liquids Using a QSPR Model
journal, November 2007

  • Tochigi, Katsumi; Yamamoto, Hiroshi
  • The Journal of Physical Chemistry C, Vol. 111, Issue 43
  • DOI: 10.1021/jp073839a

A “non-linear” quantitative structure–property relationship for the prediction of electrical conductivity of ionic liquids
journal, September 2013

  • Gharagheizi, Farhad; Sattari, Mehdi; Ilani-Kashkouli, Poorandokht
  • Chemical Engineering Science, Vol. 101
  • DOI: 10.1016/j.ces.2013.07.007

Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor
journal, November 2007


QSPR correlation for conductivities and viscosities of low-temperature melting ionic liquids
journal, January 2008

  • Bini, Riccardo; Malvaldi, Marco; Pitner, William R.
  • Journal of Physical Organic Chemistry, Vol. 21, Issue 7-8
  • DOI: 10.1002/poc.1337

In Silico Prediction of Melting Points of Ionic Liquids by Using Multilayer Perceptron Neural Networks
journal, February 2012

  • Fatemi, Mohammad H.; Izadian, Parisa
  • Journal of Theoretical and Computational Chemistry, Vol. 11, Issue 01
  • DOI: 10.1142/S0219633612500083

A review on the transport properties of ionic liquids
journal, May 2014