Machine learning predictions of diffusion in bulk and confined ionic liquids using simple descriptors

Bobbitt, N. Scott; Allers, Joshua P.; Harvey, Jacob A.; Poe, Derrick; Wemhoner, Jordyn D.; Keth, Jane; Greathouse, Jeffery A.

doi:10.1039/d3me00033h

Machine learning predictions of diffusion in bulk and confined ionic liquids using simple descriptors

Journal Article · Tue Jun 13 00:00:00 EDT 2023 · Molecular Systems Design & Engineering

DOI:https://doi.org/10.1039/d3me00033h· OSTI ID:2439910

^[1]; Allers, Joshua P. ^[1]; ^[1]; Poe, Derrick ^[2]; ^[1]; Keth, Jane ^[1]; ^[1]

Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States)

Ionic liquids have many intriguing properties and widespread applications such as separations and energy storage. However, ionic liquids are complex fluids and predicting their behavior is difficult, particularly in confined environments. We introduce fast and computationally efficient machine learning (ML) models that can predict diffusion coefficients and ionic conductivity of bulk and nanoconfined ionic liquids over a wide temperature range (350–500 K). The ML models are trained on molecular dynamics simulation data for 29 unique ionic liquids as bulk fluids and confined in graphite slit pores. This model is based on simple physical descriptors of the cations and anions such as molecular weight and surface area. Here, we also demonstrate that accurate results can be obtained using only descriptors derived from SMILES (simplified molecular-input line-entry system) codes for the ions with minimal computational effort. This offers a fast and efficient method for estimating diffusion and conductivity of nanoconfined ionic liquids at various temperatures without the need for expensive molecular dynamics simulations.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 2439910

Alternate ID(s):: OSTI ID: 1984898

Report Number(s):: SAND--2024-11607J

Journal Information:: Molecular Systems Design & Engineering, Journal Name: Molecular Systems Design & Engineering Journal Issue: 10 Vol. 8; ISSN 2058-9689

Publisher:: Royal Society of ChemistryCopyright Statement

Country of Publication:: United States

Language:: English

References (91)

Electrochemical Aspects of Ionic Liquids Ohno, Hiroyuki John Wiley & Sons, Inc. https://doi.org/10.1002/0471762512	book	April 2005
Introduction of an Ionic Liquid into the Micropores of a Metal-Organic Framework and Its Anomalous Phase Behavior Fujie, Kazuyuki; Yamada, Teppei; Ikeda, Ryuichi Angewandte Chemie International Edition, Vol. 53, Issue 42 https://doi.org/10.1002/anie.201406011	journal	August 2014
Packing optimization for automated generation of complex system's initial configurations for molecular dynamics and docking Martínez, José Mario; Martínez, Leandro Journal of Computational Chemistry, Vol. 24, Issue 7 https://doi.org/10.1002/jcc.10216	journal	May 2003
Development and testing of a general amber force field Wang, Junmei; Wolf, Romain M.; Caldwell, James W. Journal of Computational Chemistry, Vol. 25, Issue 9 https://doi.org/10.1002/jcc.20035	journal	January 2004
PACKMOL: A package for building initial configurations for molecular dynamics simulations Martínez, L.; Andrade, R.; Birgin, E. G. Journal of Computational Chemistry, Vol. 30, Issue 13 https://doi.org/10.1002/jcc.21224	journal	October 2009
Controlling the Mobility of Ionic Liquids in the Nanopores of MOFs by Adjusting the Pore Size: From Conduction Collapse by Mutual Pore Blocking to Unhindered Ion Transport Zhang, Zejun; Liu, Modan; Li, Chun Small, Vol. 18, Issue 39 https://doi.org/10.1002/smll.202200602	journal	August 2022
Thermophysical Properties of Ionic Liquids Rooney, David; Jacquemin, Johan; Gardas, Ramesh Topics in Current Chemistry https://doi.org/10.1007/128_2008_32	book	January 2009
Specific distributions of anions and cations of an ionic liquid through confinement between graphene sheets Alibalazadeh, Mahtab; Foroutan, Masumeh Journal of Molecular Modeling, Vol. 21, Issue 7 https://doi.org/10.1007/s00894-015-2703-4	journal	June 2015
Correlation and Prediction of Dense Fluid Transport Coefficients. IX. Ionic Liquids Gaciño, Félix M.; Comuñas, María J. P.; Fernández, Josefa International Journal of Thermophysics, Vol. 35, Issue 5 https://doi.org/10.1007/s10765-014-1626-0	journal	May 2014
Graph theory and molecular orbitals. Total φ-electron energy of alternant hydrocarbons Gutman, I.; Trinajstić, N. Chemical Physics Letters, Vol. 17, Issue 4 https://doi.org/10.1016/0009-2614(72)85099-1	journal	December 1972
Large-scale QSAR study of aromatase inhibitors using SMILES-based descriptors Worachartcheewan, Apilak; Mandi, Prasit; Prachayasittikul, Virapong Chemometrics and Intelligent Laboratory Systems, Vol. 138 https://doi.org/10.1016/j.chemolab.2014.07.017	journal	November 2014
Ionic liquids in gas separation processing Shang, Dawei; Liu, Xinyan; Bai, Lu Current Opinion in Green and Sustainable Chemistry, Vol. 5 https://doi.org/10.1016/j.cogsc.2017.03.015	journal	June 2017
LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales Thompson, Aidan P.; Aktulga, H. Metin; Berger, Richard Computer Physics Communications, Vol. 271 https://doi.org/10.1016/j.cpc.2021.108171	journal	February 2022
Atom–bond connectivity and the energetic of branched alkanes Estrada, Ernesto Chemical Physics Letters, Vol. 463, Issue 4-6 https://doi.org/10.1016/j.cplett.2008.08.074	journal	October 2008
On difference of Zagreb indices Furtula, Boris; Gutman, Ivan; Ediz, Süleyman Discrete Applied Mathematics, Vol. 178 https://doi.org/10.1016/j.dam.2014.06.011	journal	December 2014
Towards predicting liquid fuel physicochemical properties using molecular dynamics guided machine learning models Freitas, Rodolfo S. M.; Lima, Ágatha P. F.; Chen, Cheng Fuel, Vol. 329 https://doi.org/10.1016/j.fuel.2022.125415	journal	December 2022
Combination of ionic liquids with membrane technology: A new approach for CO2 separation Dai, Zhongde; Noble, Richard D.; Gin, Douglas L. Journal of Membrane Science, Vol. 497 https://doi.org/10.1016/j.memsci.2015.08.060	journal	January 2016
Nano-confined ionic liquid [emim][PF6] between graphite sheets: A molecular dynamics study Salemi, Sirous; Akbarzadeh, Hamed; Abdollahzadeh, Somayeh Journal of Molecular Liquids, Vol. 215 https://doi.org/10.1016/j.molliq.2016.01.035	journal	March 2016
A novel correlative approach for ionic liquid thermal conductivities Haghbakhsh, Reza; Raeissi, Sona Journal of Molecular Liquids, Vol. 236 https://doi.org/10.1016/j.molliq.2017.03.117	journal	June 2017
Development of a group contribution method for estimating surface tension of ionic liquids over a wide range of temperatures Lazzús, Juan A.; Pulgar-Villarroel, Geraldo; Cuturrufo, Fernando Journal of Molecular Liquids, Vol. 240 https://doi.org/10.1016/j.molliq.2017.05.095	journal	August 2017
Predicting ionic liquid melting points using machine learning Venkatraman, Vishwesh; Evjen, Sigvart; Knuutila, Hanna K. Journal of Molecular Liquids, Vol. 264 https://doi.org/10.1016/j.molliq.2018.03.090	journal	August 2018
In silico prediction and experimental verification of ionic liquid refractive indices Venkatraman, Vishwesh; Raj, Jaganathan Joshua; Evjen, Sigvart Journal of Molecular Liquids, Vol. 264 https://doi.org/10.1016/j.molliq.2018.05.067	journal	August 2018
Ionic liquids synthesis and applications: An overview Singh, Sandip K.; Savoy, Anthony W. Journal of Molecular Liquids, Vol. 297 https://doi.org/10.1016/j.molliq.2019.112038	journal	January 2020
Nanoconfined ionic liquids: A computational study Otero-Mato, José M.; Montes-Campos, Hadrián; Cabeza, Oscar Journal of Molecular Liquids, Vol. 320 https://doi.org/10.1016/j.molliq.2020.114446	journal	December 2020
Quantitative structure-property relationship for predicting the diffusion coefficient of ionic liquids Xiao, Yongjun; Song, Fan; An, Shuhao Journal of Molecular Liquids, Vol. 349 https://doi.org/10.1016/j.molliq.2022.118476	journal	March 2022
Diverse applications of ionic liquids: A comprehensive review Kaur, Gagandeep; Kumar, Harsh; Singla, Meenu Journal of Molecular Liquids, Vol. 351 https://doi.org/10.1016/j.molliq.2022.118556	journal	April 2022
Ionic liquids confined in porous matrices: Physicochemical properties and applications Singh, Manish Pratap; Singh, Rajendra Kumar; Chandra, Suresh Progress in Materials Science, Vol. 64 https://doi.org/10.1016/j.pmatsci.2014.03.001	journal	July 2014
Efficient separation of CO2/CH4 by ionic liquids confined in graphene oxide: A molecular dynamics simulation Yan, Fang; Guo, Yandong; Wang, Zhenlei Separation and Purification Technology, Vol. 289 https://doi.org/10.1016/j.seppur.2022.120736	journal	May 2022
NeuroFramework: A package based on neuroevolutionary algorithms to estimate the melting temperature of ionic liquids Cerecedo-Cordoba, Jorge Alberto; Frausto-Solís, Juan; González Barbosa, Juan Javier SoftwareX, Vol. 11 https://doi.org/10.1016/j.softx.2020.100448	journal	January 2020
Nanoconfined Ionic Liquids Zhang, Shiguo; Zhang, Jiaheng; Zhang, Yan Chemical Reviews, Vol. 117, Issue 10 https://doi.org/10.1021/acs.chemrev.6b00509	journal	December 2016
Molecular Simulation Study of the Performance of Supported Ionic Liquid Phase Materials for the Separation of Carbon Dioxide from Methane and Hydrogen Budhathoki, Samir; Shah, Jindal K.; Maginn, Edward J. Industrial & Engineering Chemistry Research, Vol. 56, Issue 23 https://doi.org/10.1021/acs.iecr.7b00763	journal	June 2017
Extensive Databases and Group Contribution QSPRs of Ionic Liquids Properties. 1. Density Paduszyński, Kamil Industrial & Engineering Chemistry Research, Vol. 58, Issue 13 https://doi.org/10.1021/acs.iecr.9b00130	journal	March 2019
Extensive Databases and Group Contribution QSPRs of Ionic Liquids Properties. 2. Viscosity Paduszyński, Kamil Industrial & Engineering Chemistry Research, Vol. 58, Issue 36 https://doi.org/10.1021/acs.iecr.9b03150	journal	August 2019
Fantastic Liquids and Where To Find Them: Optimizations of Discrete Chemical Space Beckner, Wesley; Pfaendtner, Jim Journal of Chemical Information and Modeling, Vol. 59, Issue 6 https://doi.org/10.1021/acs.jcim.9b00087	journal	April 2019
Diffusion under Confinement: Hydrodynamic Finite-Size Effects in Simulation Simonnin, Pauline; Noetinger, Benoı̂t; Nieto-Draghi, Carlos Journal of Chemical Theory and Computation, Vol. 13, Issue 6 https://doi.org/10.1021/acs.jctc.7b00342	journal	June 2017
Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids Allers, Joshua P.; Priest, Chad W.; Greathouse, Jeffery A. The Journal of Physical Chemistry B, Vol. 125, Issue 47 https://doi.org/10.1021/acs.jpcb.1c07092	journal	November 2021
NMR and Theoretical Study of In-Pore Diffusivity of Ionic Liquid–Solvent Mixtures Cui, Jinlei; Lin, Xiaobo; Zhao, Wei The Journal of Physical Chemistry B, Vol. 126, Issue 26 https://doi.org/10.1021/acs.jpcb.2c00860	journal	June 2022
Prediction of Self-Diffusion in Binary Fluid Mixtures Using Artificial Neural Networks Allers, Joshua P.; Keth, Jane; Alam, Todd M. The Journal of Physical Chemistry B, Vol. 126, Issue 24 https://doi.org/10.1021/acs.jpcb.2c01723	journal	June 2022
Molecular and Artificial Neural Networks Modeling of Sorption and Diffusion of Small Alkanes, Alkenes and Their Ternary Mixtures in ZIF-8 at Different Temperatures Gkourras, Arsenios; Gergidis, Leonidas N. The Journal of Physical Chemistry B, Vol. 126, Issue 29 https://doi.org/10.1021/acs.jpcb.2c03478	journal	July 2022
Machine Learning Self-Diffusion Prediction for Lennard-Jones Fluids in Pores Leverant, Calen J.; Harvey, Jacob A.; Alam, Todd M. The Journal of Physical Chemistry C, Vol. 125, Issue 46 https://doi.org/10.1021/acs.jpcc.1c08297	journal	November 2021
Temperature-Induced Molecular Rearrangement of an Ionic Liquid Confined in Nanospaces: An in Situ X-ray Absorption Fine Structure Study Jiang, Fangling; Li, Cheng; Fu, Haiying The Journal of Physical Chemistry C, Vol. 119, Issue 39 https://doi.org/10.1021/acs.jpcc.5b07325	journal	September 2015
Structural Anomalies and Electronic Properties of an Ionic Liquid under Nanoscale Confinement Pham, Tuan Anh; Coulthard, Riley M.; Zobel, Mirijam The Journal of Physical Chemistry Letters, Vol. 11, Issue 15 https://doi.org/10.1021/acs.jpclett.0c01810	journal	July 2020
Machine Learning-Based Upscaling of Finite-Size Molecular Dynamics Diffusion Simulations for Binary Fluids Leverant, Calen J.; Harvey, Jacob A.; Alam, Todd M. The Journal of Physical Chemistry Letters, Vol. 11, Issue 24 https://doi.org/10.1021/acs.jpclett.0c03108	journal	November 2020
Abnormal Enhanced Free Ions of Ionic Liquids Confined in Carbon Nanochannels Wang, Mi; Wang, Yanlei; Wang, Chenlu The Journal of Physical Chemistry Letters, Vol. 12, Issue 26 https://doi.org/10.1021/acs.jpclett.1c01114	journal	June 2021
Direct Correlation between Ionic Liquid Transport Properties and Ion Pair Lifetimes: A Molecular Dynamics Study Zhang, Yong; Maginn, Edward J. The Journal of Physical Chemistry Letters, Vol. 6, Issue 4 https://doi.org/10.1021/acs.jpclett.5b00003	journal	February 2015
Solid-Like Ordering of Imidazolium-Based Ionic Liquids at Rough Nanostructured Oxidized Silicon Surfaces Borghi, Francesca; Milani, Paolo; Podestà, Alessandro Langmuir, Vol. 35, Issue 36 https://doi.org/10.1021/acs.langmuir.9b01668	journal	August 2019
Bunching and Immobilization of Ionic Liquids in Nanoporous Metal–Organic Framework Kanj, Anemar Bruno; Verma, Rupal; Liu, Modan Nano Letters, Vol. 19, Issue 3 https://doi.org/10.1021/acs.nanolett.8b04694	journal	February 2019
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules Weininger, David Journal of Chemical Information and Modeling, Vol. 28, Issue 1 https://doi.org/10.1021/ci00057a005	journal	February 1988
SMILES. 2. Algorithm for generation of unique SMILES notation Weininger, David; Weininger, Arthur; Weininger, Joseph L. Journal of Chemical Information and Modeling, Vol. 29, Issue 2 https://doi.org/10.1021/ci00062a008	journal	May 1989
Densification of Ionic Liquid Molecules within a Hierarchical Nanoporous Carbon Structure Revealed by Small-Angle Scattering and Molecular Dynamics Simulation Bañuelos, José Leobardo; Feng, Guang; Fulvio, Pasquale F. Chemistry of Materials, Vol. 26, Issue 2 https://doi.org/10.1021/cm4035159	journal	December 2013
Morphology and Melting Behavior of Ionic Liquids inside Single-Walled Carbon Nanotubes Chen, Shimou; Kobayashi, Keita; Miyata, Yasumitsu Journal of the American Chemical Society, Vol. 131, Issue 41 https://doi.org/10.1021/ja904283d	journal	October 2009
NMR Study of Ion Dynamics and Charge Storage in Ionic Liquid Supercapacitors Forse, Alexander C.; Griffin, John M.; Merlet, Céline Journal of the American Chemical Society, Vol. 137, Issue 22 https://doi.org/10.1021/jacs.5b03958	journal	May 2015
Structure and Dynamics of a Confined Ionic Liquid. Topics of Relevance to Dye-Sensitized Solar Cells Pinilla, Carlos; Del Pópolo, Mario G.; Lynden-Bell, Ruth M. The Journal of Physical Chemistry B, Vol. 109, Issue 38 https://doi.org/10.1021/jp052999o	journal	September 2005
A Simple AIMD Approach to Derive Atomic Charges for Condensed Phase Simulation of Ionic Liquids Zhang, Yong; Maginn, Edward J. The Journal of Physical Chemistry B, Vol. 116, Issue 33 https://doi.org/10.1021/jp3037999	journal	August 2012
Pt₂Cl₈^2– Dimer Formation of [Bmim]₂PtCl₄ Ionic Liquid When Confined in Silica Nanopores Li, Cheng; Wang, Yaxing; Guo, Xiaojing The Journal of Physical Chemistry C, Vol. 118, Issue 6 https://doi.org/10.1021/jp4115942	journal	February 2014
Unravelling the Role of the Compressed Gas on Melting Point of Liquid Confined in Nanospace Chen, Shimou; Liu, Yusheng; Fu, Haiying The Journal of Physical Chemistry Letters, Vol. 3, Issue 8 https://doi.org/10.1021/jz300225n	journal	March 2012
Morphological Control of Room-Temperature Ionic Liquid Templated Mesoporous Silica Nanoparticles for Controlled Release of Antibacterial Agents Trewyn, Brian G.; Whitman, Chad M.; Lin, Victor S. -Y. Nano Letters, Vol. 4, Issue 11 https://doi.org/10.1021/nl048774r	journal	November 2004
Structure of graphite by neutron diffraction Trucano, Peter; Chen, Ruey Nature, Vol. 258, Issue 5531 https://doi.org/10.1038/258136a0	journal	November 1975
Materials for electrochemical capacitors Simon, Patrice; Gogotsi, Yury Nature Materials, Vol. 7, Issue 11 https://doi.org/10.1038/nmat2297	journal	November 2008
Ionic-liquid materials for the electrochemical challenges of the future Armand, Michel; Endres, Frank; MacFarlane, Douglas R. Nature Materials, Vol. 8, Issue 8, p. 621-629 https://doi.org/10.1038/nmat2448	journal	July 2009
Partial breaking of the Coulombic ordering of ionic liquids confined in carbon nanopores Futamura, Ryusuke; Iiyama, Taku; Takasaki, Yuma Nature Materials, Vol. 16, Issue 12 https://doi.org/10.1038/nmat4974	journal	September 2017
Applications of ionic liquids in the chemical industry Plechkova, Natalia V.; Seddon, Kenneth R. Chem. Soc. Rev., Vol. 37, Issue 1 https://doi.org/10.1039/B006677J	journal	January 2008
Ionogels as drug delivery system: one-step sol–gel synthesis using imidazolium ibuprofenate ionic liquid Viau, Lydie; Tourné-Péteilh, Corine; Devoisselle, Jean-Marie Chem. Commun., Vol. 46, Issue 2 https://doi.org/10.1039/B913879J	journal	January 2010
Ionic liquids studied across different scales: A computational perspective Wendler, Katharina; Dommert, Florian; Zhao, Yuan Yuan Faraday Discuss., Vol. 154 https://doi.org/10.1039/C1FD00051A	journal	January 2012
Molecular dynamics study of the effect of alkyl chain length on melting points of [C _n MIM][PF ₆ ] ionic liquids Zhang, Yong; Maginn, Edward J. Phys. Chem. Chem. Phys., Vol. 16, Issue 26 https://doi.org/10.1039/C4CP01048E	journal	January 2014
ABCluster: the artificial bee colony algorithm for cluster global optimization Zhang, Jun; Dolg, Michael Physical Chemistry Chemical Physics, Vol. 17, Issue 37 https://doi.org/10.1039/C5CP04060D	journal	January 2015
Height-driven structure and thermodynamic properties of confined ionic liquids inside carbon nanochannels from molecular dynamics study Wang, Chenlu; Wang, Yanlei; Lu, Yumiao Physical Chemistry Chemical Physics, Vol. 21, Issue 24 https://doi.org/10.1039/C9CP00732F	journal	January 2019
Rapid, comprehensive screening of ionic liquids towards sustainable applications Venkatraman, Vishwesh; Evjen, Sigvart; Lethesh, Kallidanthiyil Chellappan Sustainable Energy & Fuels, Vol. 3, Issue 10 https://doi.org/10.1039/C9SE00472F	journal	January 2019
Artificial neural network prediction of self-diffusion in pure compounds over multiple phase regimes Allers, Joshua P.; Garzon, Fernando H.; Alam, Todd M. Physical Chemistry Chemical Physics, Vol. 23, Issue 8 https://doi.org/10.1039/D0CP06693A	journal	January 2021
A review on machine learning algorithms for the ionic liquid chemical space Koutsoukos, Spyridon; Philippi, Frederik; Malaret, Francisco Chemical Science, Vol. 12, Issue 20 https://doi.org/10.1039/D1SC01000J	journal	January 2021
A generalized machine learning model for predicting ionic conductivity of ionic liquids Dhakal, Pratik; Shah, Jindal K. Molecular Systems Design & Engineering, Vol. 7, Issue 10 https://doi.org/10.1039/D2ME00046F	journal	January 2022
Compression of ionic liquid when confined in porous silica nanoparticles Li, Cheng; Guo, Xiaojing; He, Yaxing RSC Advances, Vol. 3, Issue 25 https://doi.org/10.1039/c3ra40245b	journal	January 2013
Polymorphic transitions in single crystals: A new molecular dynamics method Parrinello, M.; Rahman, A. Journal of Applied Physics, Vol. 52, Issue 12 https://doi.org/10.1063/1.328693	journal	December 1981
Constant pressure molecular dynamics algorithms Martyna, Glenn J.; Tobias, Douglas J.; Klein, Michael L. The Journal of Chemical Physics, Vol. 101, Issue 5 https://doi.org/10.1063/1.467468	journal	September 1994
Machine learning prediction of self-diffusion in Lennard-Jones fluids Allers, Joshua P.; Harvey, Jacob A.; Garzon, Fernando H. The Journal of Chemical Physics, Vol. 153, Issue 3 https://doi.org/10.1063/5.0011512	journal	July 2020
Slow liquid dynamics near solid surfaces: Insights from site-resolved studies of ionic liquids in silica confinement Köster, Robin; Vogel, Michael The Journal of Chemical Physics, Vol. 156, Issue 7 https://doi.org/10.1063/5.0079722	journal	February 2022
Conductivity prediction model for ionic liquids using machine learning Datta, R.; Ramprasad, R.; Venkatram, S. The Journal of Chemical Physics, Vol. 156, Issue 21 https://doi.org/10.1063/5.0089568	journal	June 2022
How do external forces related to mass and charge affect the structures and dynamics of an ionic liquid? Guan, Yongji; Clark, Ryan; Philippi, Frederik The Journal of Chemical Physics, Vol. 156, Issue 20 https://doi.org/10.1063/5.0091322	journal	May 2022
Symbolic regression development of empirical equations for diffusion in Lennard-Jones fluids Alam, Todd M.; Allers, Joshua P.; Leverant, Calen J. The Journal of Chemical Physics, Vol. 157, Issue 1 https://doi.org/10.1063/5.0093658	journal	July 2022
Ionic liquid confined in silica nanopores: molecular dynamics in the isobaric–isothermal ensemble Ori, Guido; Villemot, F.; Viau, L. Molecular Physics, Vol. 112, Issue 9-10 https://doi.org/10.1080/00268976.2014.902138	journal	February 2014
ChemoPy: freely available python package for computational biology and chemoinformatics Cao, Dong-Sheng; Xu, Qing-Song; Hu, Qian-Nan Bioinformatics, Vol. 29, Issue 8 https://doi.org/10.1093/bioinformatics/btt105	journal	March 2013
Rapid estimation of elastic constants by molecular dynamics simulation under constant stress Shinoda, Wataru; Shiga, Motoyuki; Mikami, Masuhiro Physical Review B, Vol. 69, Issue 13 https://doi.org/10.1103/PhysRevB.69.134103	journal	April 2004
How Stickiness Can Speed Up Diffusion in Confined Systems Alexandre, A.; Mangeat, M.; Guérin, T. Physical Review Letters, Vol. 128, Issue 21 https://doi.org/10.1103/PhysRevLett.128.210601	journal	May 2022
Ionic correlations and failure of Nernst-Einstein relation in solid-state electrolytes Marcolongo, Aris; Marzari, Nicola Physical Review Materials, Vol. 1, Issue 2 https://doi.org/10.1103/PhysRevMaterials.1.025402	journal	July 2017
Solvent-accessible surfaces of proteins and nucleic acids Connolly, M. Science, Vol. 221, Issue 4612 https://doi.org/10.1126/science.6879170	journal	August 1983
The Second Hyper-Zagreb Coindex of Chemical Graphs and Some Applications Ayache, Ahmed; Alameri, Abdu; Alsharafi, Mohammed Journal of Chemistry, Vol. 2021 https://doi.org/10.1155/2021/3687533	journal	November 2021
Mordred: a molecular descriptor calculator Moriwaki, Hirotomo; Tian, Yu-Shi; Kawashita, Norihito Journal of Cheminformatics, Vol. 10, Issue 1 https://doi.org/10.1186/s13321-018-0258-y	journal	February 2018
Application of polymer-based membranes containing ionic liquids in membrane separation processes: a critical review Rynkowska, Edyta; Fatyeyeva, Kateryna; Kujawski, Wojciech Reviews in Chemical Engineering, Vol. 34, Issue 3 https://doi.org/10.1515/revce-2016-0054	journal	May 2017
The Generalized Zagreb Index of Some Carbon Structures Sarkar, Prosanta; De, Nilanjan; Pal, Anita Acta Chemica Iasi, Vol. 26, Issue 1 https://doi.org/10.2478/achi-2018-0007	journal	July 2018
The Ionic Liquid Property Explorer: An Extensive Library of Task-Specific Solvents Venkatraman, Vishwesh; Evjen, Sigvart; Chellappan Lethesh, Kallidanthiyil Data, Vol. 4, Issue 2 https://doi.org/10.3390/data4020088	journal	June 2019
The Electrical Conductivity of Ionic Liquids: Numerical and Analytical Machine Learning Approaches Karakasidis, Theodoros E.; Sofos, Filippos; Tsonos, Christos Fluids, Vol. 7, Issue 10 https://doi.org/10.3390/fluids7100321	journal	October 2022

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