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

Martin, Shawn; Pratt, III, Harry D.; Anderson, Travis M.

doi:10.1002/minf.201600125

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:

Martin, Shawn ^[1]; Pratt, III, Harry D. ^[1]; Anderson, Travis M. ^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Publication Date:: Tue Feb 21 00:00:00 EST 2017

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.

Copy to clipboard


                    Martin, Shawn, Pratt, III, Harry D., & Anderson, Travis M. Screening for High Conductivity/Low Viscosity Ionic Liquids Using Product Descriptors.  United States.  https://doi.org/10.1002/minf.201600125

Copy to clipboard


                    Martin, Shawn, Pratt, III, Harry D., and Anderson, Travis M. Tue .  
"Screening for High Conductivity/Low Viscosity Ionic Liquids Using Product Descriptors".  United States.  https://doi.org/10.1002/minf.201600125.  https://www.osti.gov/servlets/purl/1375027.

Copy to clipboard


                    
@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         = {Tue Feb 21 00:00:00 EST 2017},

  month        = {Tue Feb 21 00:00:00 EST 2017}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1002/minf.201600125

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 16 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

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

Sawant, A. D.; Raut, D. G.; Darvatkar, N. B.
Green Chemistry Letters and Reviews, Vol. 4, Issue 1
DOI: 10.1080/17518253.2010.500622

Gene Selection for Cancer Classification using Support Vector Machines
journal, January 2002

Guyon, Isabelle; Weston, Jason; Barnhill, Stephen
Machine Learning, Vol. 46, Issue 1/3, p. 389-422
DOI: 10.1023/A:1012487302797

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. Green solvents for the future
journal, January 2000

Earle, Martyn J.; Seddon, Kenneth R.
Pure and Applied Chemistry, Vol. 72, Issue 7
DOI: 10.1351/pac200072071391

Preparation of Inorganic Materials Using Ionic Liquids
journal, January 2010

Ma, Zhen; Yu, Jihong; Dai, Sheng
Advanced Materials, Vol. 22, Issue 2
DOI: 10.1002/adma.200900603

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

Martin, S.; Roe, D.; Faulon, J. -L.
Bioinformatics, Vol. 21, Issue 2
DOI: 10.1093/bioinformatics/bth483

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

Das, Rudra Narayan; Roy, Kunal
Molecular Diversity, Vol. 17, Issue 1
DOI: 10.1007/s11030-012-9413-y

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

Han, Chao; Yu, Guangren; Wen, Lu
Fluid Phase Equilibria, Vol. 300, Issue 1-2
DOI: 10.1016/j.fluid.2010.10.021

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

Faulon, Jean-Loup; Misra, Milind; Martin, Shawn
Bioinformatics, Vol. 24, Issue 2
DOI: 10.1093/bioinformatics/btm580

Least Squares Support Vector Machine Classifiers
journal, June 1999

Suykens, J. A. K.; Vandewalle, J.
Neural Processing Letters, Vol. 9, Issue 3, p. 293-300
DOI: 10.1023/A:1018628609742

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

Wang, Xiaojing; Chi, Yanling; Mu, Tiancheng
Journal of Molecular Liquids, Vol. 193
DOI: 10.1016/j.molliq.2014.03.011

Works referencing / citing this record:

Chemoinformatic Approaches To Predict the Viscosities of Ionic Liquids and Ionic Liquid‐Containing Systems
journal, September 2019

Carrera, Gonçalo V. S. M.; Nunes da Ponte, Manuel; Rebelo, Luís P. N.
ChemPhysChem, Vol. 20, Issue 21
DOI: 10.1002/cphc.201900593

Similar Records in DOE PAGES and OSTI.GOV collections:

Screening Ionic Liquids Based on Ionic Volume and Electrostatic Potential Analyses

Journal Article Liu, Xiaoyang ; O’Harra, Kathryn E. ; Bara, Jason E. ; ... - Journal of Physical Chemistry. B, Condensed Matter, Materials, Surfaces, Interfaces and Biophysical Chemistry

Ionic liquids are known to have tunable solvation properties, based on the pairing of different anions and cations, but the compositional landscape is vast and challenging to navigate efficiently. Some computational screening protocols are available, but they can be either time-consuming or difficult to implement. Herein, we perform a detailed investigation of the fundamental role of electrostatic interactions in these systems. We effectively develop a bridge between the previous volume-based approach (VBA) with a quantum structure–property relationship (QSPR) approach to create fast, simple screening guidelines. We propose a new parameter that is applicable to both monovalent and multivalent ions, the ionic polarity index (IPI), which is defined as the ratio of the average electrostatic surface potential (more »« less
https://doi.org/10.1021/acs.jpcb.0c10259

Full Text Available
Synthesis and characterization of low viscosity hexafluoroacetylacetonate-based hydrophobic magnetic ionic liquids

Journal Article Pierson, Stephen A. ; Nacham, Omprakash ; Clark, Kevin D. ; ... - New Journal of Chemistry

Magnetic ionic liquids (MILs) are distinguished from traditional ionic liquids (ILs) by the incorporation of a paramagnetic component within their chemical structure. Hydrophobic MILs are novel solvents that can be used in many applications, including liquid–liquid extraction (LLE) and catalysis. Low viscosity and low water solubility are essential features that determine their feasibility in LLE. Here, we synthesized extremely hydrophobic MILs by using transition and rare earth metal hexafluoroacetylacetonate chelated anions paired with the trihexyl(tetradecyl)phosphonium ([P₆₆₆₁₄⁺]) cation. Hydrophobic MILs exhibiting water solubilities less than 0.01% (v/v) were synthesized in a rapid two-step procedure. Furthermore, the viscosities of the MILs aremore »« less
https://doi.org/10.1039/c7nj00206h
Ultra-Low Viscosity and High Magnetic Susceptibility Magnetic Ionic Liquids Featuring Functionalized Diglycolic Acid Ester Rare-Earth and Transition Metal Chelates

Journal Article Abbasi, Nabeel Mujtaba ; De Silva, Shashini ; Biswas, Anis ; ... - ACS Omega

Magnetic ionic liquids (MILs) comprise a subcategory of ionic liquids (ILs) and contain a paramagnetic metal center allowing them to be readily manipulated by an external magnetic field. While MILs are popularly employed as solvents in catalysis, separations, and organic synthesis, most low viscosity combinations possess a hydrophilic character that limits their use in aqueous matrices. To date, no study has reported the synthesis and characterization of hydrophobic MILs with viscosities similar to those of hydrophilic MILs and organic solvents while simultaneously exhibiting enhanced magnetic and thermal properties. In this study, diglycolic acid esters are employed as ligands to chelatemore »« less
https://doi.org/10.1021/acsomega.3c03938

Full Text Available
Anion-Functionalized Task-Specific Ionic Liquids: Molecular Origin of Change in Viscosity upon CO₂ Capture

Journal Article Li, Ailin ; Tian, Ziqi ; Yan, Tianying ; ... - Journal of Physical Chemistry. B, Condensed Matter, Materials, Surfaces, Interfaces and Biophysical Chemistry

The structure and dynamics of a task-specific ionic liquid (TSIL), trihexyl(tetradecyl)phosphonium imidazolate, before and after absorbing CO₂ were studied with a molecular dynamics (MD) simulation. This particular ionic liquid is one of several newly discovered azole-based TSILs for equimolar CO₂ capture. Unlike other TSILs whose viscosity increases drastically upon reaction with CO₂, its viscosity decreases after CO₂ absorption. This unique behavior was confirmed in our MD simulation. In this study, we find that after CO₂ absorption the translational dynamics of the whole system is accelerated, accompanied by an accelerated rotational dynamics of the cations. Radial distribution function and spatial distributionmore »« less
https://doi.org/10.1021/jp5100236
Amine-functionalized task-specific ionic liquids: a mechanistic explanation for the dramatic increase in viscosity upon complexation with CO{sub 2} from molecular simulation

Journal Article Gutowski, K E ; Maginn, E J - Journal of the American Chemical Society

The capture of CO{sub 2} from fossil fuel combustion, particularly in coal-fired power plants, represents a critical component of efforts aimed at stabilizing greenhouse gas levels in the atmosphere. Recently, a series of second-generation task-specific ionic liquids (TSILs) containing amine functional groups have been synthesized and demonstrated to have much higher capacities for CO{sub 2} due to their reactivity with CO{sub 2}, as well unusually high viscosities in both the neat and complexed states. The current work extends the seminal studies of CO{sub 2} capture with ionic liquids (ILs) by providing insight from simulations into the mechanism responsible for themore »« less
https://doi.org/10.1021/ja804654b

Similar Records

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

Abstract

Citation Formats

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

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

Gene Selection for Cancer Classification using Support Vector Machines journal, January 2002

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Least Squares Support Vector Machine Classifiers journal, June 1999

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

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

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

Chemoinformatic Approaches To Predict the Viscosities of Ionic Liquids and Ionic Liquid‐Containing Systems journal, September 2019

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

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

Gene Selection for Cancer Classification using Support Vector Machines
journal, January 2002

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Least Squares Support Vector Machine Classifiers
journal, June 1999

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

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

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

Chemoinformatic Approaches To Predict the Viscosities of Ionic Liquids and Ionic Liquid‐Containing Systems
journal, September 2019