References
1. Zhao, Y.; Gani, R.; Afzal, R. M.; Zhang, X.; Zhang, S., Ionic liquids
for absorption and separation of gases: An extensive database and a
systematic screening method. AIChE Journal 2017,63 (4), 1353-1367.
2. Zhang, X.; Ding, X.; Song, Z.; Zhou, T.; Sundmacher, K., Integrated
ionic liquid and rate‐based absorption process design for gas
separation: Global optimization using hybrid models. AIChE
Journal 2021, 67 (10).
3. Beil, S.; Markiewicz, M.; Pereira, C. S.; Stepnowski, P.; Thöming,
J.; Stolte, S., Toward the Proactive Design of Sustainable Chemicals:
Ionic Liquids as a Prime Example. Chemical Reviews 2021,121 (21), 13132-13173.
4. Roberts, N. J.; Lye, G. J., Application of Room-Temperature Ionic
Liquids in Biocatalysis: Opportunities and Challenges. 2002,818 , 347-359.
5. Vekariya, R. L., A review of ionic liquids: Applications towards
catalytic organic transformations. Journal of Molecular Liquids2017, 227 , 44-60.
6. Wasilewski, T.; Gębicki, J.; Kamysz, W., Prospects of ionic liquids
application in electronic and bioelectronic nose instruments. TrAC
Trends in Analytical Chemistry 2017, 93 , 23-36.
7. Watanabe, M.; Thomas, M. L.; Zhang, S.; Ueno, K.; Yasuda, T.; Dokko,
K., Application of Ionic Liquids to Energy Storage and Conversion
Materials and Devices. Chemical Reviews 2017, 117(10), 7190-7239.
8. Song, Z.; Zhang, C.; Qi, Z.; Zhou, T.; Sundmacher, K., Computer-aided
design of ionic liquids as solvents for extractive desulfurization.AIChE Journal 2018, 64 (3), 1013-1025.
9. Huang, Y.; Dong, H.; Zhang, X.; Li, C.; Zhang, S., A new fragment
contribution-corresponding states method for physicochemical properties
prediction of ionic liquids. AIChE Journal 2013,59 (4), 1348-1359.
10. Cherkasov, A.; Muratov, E. N.; Fourches, D.; Varnek, A.; Baskin, II;
Cronin, M.; Dearden, J.; Gramatica, P.; Martin, Y. C.; Todeschini, R.;
Consonni, V.; Kuz’min, V. E.; Cramer, R.; Benigni, R.; Yang, C.;
Rathman, J.; Terfloth, L.; Gasteiger, J.; Richard, A.; Tropsha, A., QSAR
modeling: where have you been? Where are you going to? J Med Chem2014, 57 (12), 4977-5010.
11. Katritzky, A. R., Kuanar, M., Slavov, S., Hall, C. D., Karelson, M.,
Kahn, I., and Dobchev, D. A., Quantitative Correlation of Physical and
Chemical Properties with Chemical Structure: Utility for Prediction.Chemical Reviews 2010, 110(10) , 5714-5789.
12. Greaves, T. L.; Drummond, C. J., Protic Ionic Liquids: Evolving
Structure–Property Relationships and Expanding Applications.Chemical Reviews 2015, 115 (20), 11379-11448.
13. Izgorodina, E. I.; Seeger, Z. L.; Scarborough, D. L. A.; Tan, S. Y.
S., Quantum Chemical Methods for the Prediction of Energetic, Physical,
and Spectroscopic Properties of Ionic Liquids. Chemical Reviews2017, 117 (10), 6696-6754.
14. Wu, K.-J.; Luo, H.; Yang, L., Structure-based model for prediction
of electrical conductivity of pure ionic liquids. AIChE Journal2016, 62 (10), 3751-3762.
15. Paduszyński, K., Extensive Databases and Group Contribution QSPRs of
Ionic Liquids Properties. 1. Density. Industrial & Engineering
Chemistry Research 2019, 58 (13), 5322-5338.
16. Das, S.; Ojha, P. K.; Roy, K., Development of a temperature
dependent 2D-QSPR model for viscosity of diverse functional ionic
liquids. Journal of Molecular Liquids 2017, 240 ,
454-467.
17. Mirkhani, S. A.; Gharagheizi, F., Predictive Quantitative
Structure–Property Relationship Model for the Estimation of Ionic
Liquid Viscosity. Industrial & Engineering Chemistry Research2012, 51 (5), 2470-2477.
18. Brauner, N.; St. Cholakov, G.; Kahrs, O.; Stateva, R. P.; Shacham,
M., Linear QSPRs for predicting pure compound properties in homologous
series. AIChE Journal 2008, 54 (4), 978-990.
19. Su, Y.; Wang, Z.; Jin, S.; Shen, W.; Ren, J.; Eden, M. R., An
architecture of deep learning in QSPR modeling for the prediction of
critical properties using molecular signatures. AIChE Journal2019, 65 (9).
20. Makarov, D. M.; Fadeeva, Y. A.; Shmukler, L. E.; Tetko, I. V.,
Beware of proper validation of models for ionic Liquids! Journal
of Molecular Liquids 2021, 344 , 117722.
21. Venkatraman, V.; Evjen, S.; Knuutila, H. K.; Fiksdahl, A.; Alsberg,
B. K., Predicting ionic liquid melting points using machine learning.Journal of Molecular Liquids 2018, 264 , 318-326.
22. Low, K.; Kobayashi, R.; Izgorodina, E. I., The effect of descriptor
choice in machine learning models for ionic liquid melting point
prediction. J Chem Phys 2020, 153 (10), 104101.
23. Yan, F.; Shi, Y.; Wang, Y.; Jia, Q.; Wang, Q.; Xia, S., QSPR models
for the properties of ionic liquids at variable temperatures based on
norm descriptors. Chemical Engineering Science 2020,217 , 115540.
24. Kang, X.; Zhao, Z.; Qian, J.; Muhammad Afzal, R., Predicting the
Viscosity of Ionic Liquids by the ELM Intelligence Algorithm.Industrial & Engineering Chemistry Research 2017,56 (39), 11344-11351.
25. Yu, G.; Zhao, D.; Wen, L.; Yang, S.; Chen, X., Viscosity of ionic
liquids: Database, observation, and quantitative structure-property
relationship analysis. AIChE Journal 2012, 58(9), 2885-2899.
26. Sattari, M.; Gharagheizi, F.; Ilani-Kashkouli, P.; Mohammadi, A. H.;
Ramjugernath, D., Estimation of the Heat Capacity of Ionic Liquids: A
Quantitative Structure–Property Relationship Approach. Industrial
& Engineering Chemistry Research 2013, 52 (36),
13217-13221.
27. Mirkhani, S. A.; Gharagheizi, F.; Farahani, N.; Tumba, K.,
Prediction of surface tension of ionic liquids by molecular approach.Journal of Molecular Liquids 2013, 179 , 78-87.
28. Farahani, N.; Gharagheizi, F.; Mirkhani, S. A.; Tumba, K., A simple
correlation for prediction of heat capacities of ionic liquids.Fluid Phase Equilibria 2013, 337 , 73-82.
29. Zhang, S.; Jia, Q.; Yan, F.; Xia, S.; Wang, Q., Evaluating the
properties of ionic liquid at variable temperatures and pressures by
quantitative structure–property relationship (QSPR). Chemical
Engineering Science 2021, 231 , 116326.
30. Shi, Y.; Li, J.-J.; Wang, Q.; Jia, Q.; Yan, F.; Luo, Z.-H.; Zhou,
Y.-N., Computer-aided estimation of kinetic rate constant for
degradation of volatile organic compounds by hydroxyl radical: An
improved model using quantum chemical and norm descriptors.Chemical Engineering Science 2022, 248 , 117244.
31. Ionic Liquids Database−ILThermo (v2.0).https://ilthermo.boulder.nist.gov/index.html(July 2, 2021).
32. Björck, Å., Least squares methods. In Handbook of Numerical
Analysis , Elsevier: 1990; Vol. 1, pp 465-652.
33. Barlow, J. L., 9 Numerical aspects of solving linear least squares
problems. In Handbook of Statistics , Elsevier: 1993; Vol. 9, pp
303-376.
34. Xiong, Y.; Ding, J.; Yu, D.; Peng, C.; Liu, H.; Hu, Y., Volumetric
Connectivity Index: A New Approach for Estimation of Density of Ionic
Liquids. Industrial & Engineering Chemistry Research2011, 50 (24), 14155-14161.
35. Alexander, D. L. J.; Tropsha, A.; Winkler, D. A., Beware of R2:
Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and
QSPR Models. Journal of Chemical Information and Modeling2015, 55 (7), 1316-1322.
36. Rücker, C.; Rücker, G.; Meringer, M., y-Randomization and its
variants in QSPR/QSAR. Journal of Chemical Information and
Modeling 2007, 47 (6), 2345-2357.
37. Yan, F.; Shang, Q.; Xia, S.; Wang, Q.; Ma, P., Application of
Topological Index in Predicting Ionic Liquids Densities by the
Quantitative Structure Property Relationship Method. Journal of
Chemical & Engineering Data 2015, 60 (3), 734-739.
38. Paduszyński, K.; Domańska, U., A New Group Contribution Method For
Prediction of Density of Pure Ionic Liquids over a Wide Range of
Temperature and Pressure. Industrial & Engineering Chemistry
Research 2011, 51 (1), 591-604.
39. Lazzús, J. A., ρ(T, p) model for ionic liquids based on quantitative
structure-property relationship calculations. Journal of Physical
Organic Chemistry 2009, 22 (12), 1193-1197.
40. Matsuda, H.; Yamamoto, H.; Kurihara, K.; Tochigi, K., Computer-aided
reverse design for ionic liquids by QSPR using descriptors of group
contribution type for ionic conductivities and viscosities. Fluid
Phase Equilibria 2007, 261 (1-2), 434-443.
41. Barycki, M.; Sosnowska, A.; Gajewicz, A.; Bobrowski, M.; Wileńska,
D.; Skurski, P.; Giełdoń, A.; Czaplewski, C.; Uhl, S.; Laux, E.;
Journot, T.; Jeandupeux, L.; Keppner, H.; Puzyn, T.,
Temperature-dependent structure-property modeling of viscosity for ionic
liquids. Fluid Phase Equilibria 2016, 427 , 9-17.
42. Yan, F.; He, W.; Jia, Q.; Wang, Q.; Xia, S.; Ma, P., Prediction of
ionic liquids viscosity at variable temperatures and pressures.Chemical Engineering Science 2018, 184 , 134-140.