[1] M. S. Mat Nor, Z. A. Manan, A. A. Mustaffa, and C. L. Suan, “An Evaluation of Thermodynamic Models for the Prediction of Solubility of Phytochemicals from Orthosiphon Staminues in Ethanol,” in Computer Aided Chemical Engineering, K. V. Gernaey, J. K. Huusom, and R. Gani, Eds., in 12 International Symposium on Process Systems Engineering and 25 European Symposium on Computer Aided Process Engineering, 37 (2015) 2087–2092. doi: 10.1016/B978-0-444-63576-1.50042-X.
[2] N. Chorbngam, R. Chawuthai, and A. Anantpinijwatna, “Novel method for properties prediction of pure organic compounds using machine learning,” in Computer Aided Chemical Engineering, M. Türkay and R. Gani, Eds., in 31 European Symposium on Computer Aided Process Engineering, 50 (2021) 431–437. doi: 10.1016/B978-0-323-88506-5.50068-1.
[3] O. Olatunji, S. Akinlabi, and N. Madushele, “Application of Artificial Intelligence in the Prediction of Thermal Properties of Biomass,” in Valorization of Biomass to Value-Added Commodities: Current Trends, Challenges, and Future Prospects, M. O. Daramola and A. O. Ayeni, Eds., in Green Energy and Technology. Cham: Springer International Publishing, (2020) 59–91. doi: 10.1007/978-3-030-38032-8_4.
[4] G. J. Maximo, N. D. D. Carareto, and M. C. Costa, “Chapter 8 - Solid–Liquid Equilibrium in Food Processes,” in Thermodynamics of Phase Equilibria in Food Engineering, C. G. Pereira, Ed., Academic Press, (2019) 335–384. doi: 10.1016/B978-0-12-811556-5.00008-9.
[5] I. Amaya, C. Jiménez, and R. Correa, “Phase Equilibrium Description of a Supercritical Extraction System Using Metaheuristic Optimization Algorithms,” in Bioinspired Heuristics for Optimization, E.-G. Talbi and A. Nakib, Eds., in Studies in Computational Intelligence. Cham: Springer International Publishing, (2019) 43–60. doi: 10.1007/978-3-319-95104-1_3.
[6] J. L. de Medeiros and O. de Q. F. Araújo, “Thermodynamic Modeling of CO2-Rich Natural Gas Fluid Systems,” in Offshore Processing of CO2-Rich Natural Gas with Supersonic Separator: Multiphase Sound Speed, CO2 Freeze-Out and HYSYS Implementation, J. L. de Medeiros, L. de Oliveira Arinelli, A. M. Teixeira, and O. de Q. F. Araújo, Eds., Cham: Springer International Publishing, (2019) 55–96. doi: 10.1007/978-3-030-04006-2_4.
[7] T.-C. Liu and S.-T. Lin, “Exact Local Composition Model for Two-Dimensional Lattice Fluids,” Ind. Eng. Chem. Res., 58 (2019) 20779–20787. doi: 10.1021/acs.iecr.9b03218.
[8] T.-C. Liu and S.-T. Lin, “A new approach for developing exact local composition models for lattice fluids,” Journal of the Taiwan Institute of Chemical Engineers, 96 (2019) 63–73. doi: 10.1016/j.jtice. 2018.11.023.
[9] G. Salgueiro, M. de Moraes, F. Pessoa, R. Cavalcante, and A. Young, “New volume translation functions for biodiesel density prediction with the Peng-Robinson Equation of state in terms of its raw materials,” Fuel, 293 (2021) 120254, Jun. 2021, doi: 10.1016/j.fuel.2021.120254.
[10] J. P. Hernández, L. A. Forero, and J. A. Velásquez, “Modelling low pressure LLE and VLE of methanol/alkane mixtures with a modified Peng-Robinson EoS and the Huron-Vidal mixing rules,” Fluid Phase Equilibria, 546 (2021) 113123. doi: 10.1016/j.fluid.2021.113123.
[11] I. Polishuk, A. Chiko, E. Cea-Klapp, and J. M. Garrido, “Implementation of CP-PC-SAFT and CS-SAFT-VR-Mie for Predicting Thermodynamic Properties of C1–C3 Halocarbon Systems. I. Pure Compounds and Mixtures with Nonassociating Compounds,” Ind. Eng. Chem. Res., vol. 60 (2021) 9624–9636. doi: 10.1021/acs.iecr.1c01700.
[12] M. Ascani and C. Held, “Prediction of salting-out in liquid-liquid two-phase systems with ePC-SAFT: Effect of the Born term and of a concentration-dependent dielectric constant,” Zeitschrift für anorganische und allgemeine Chemie, vol. 647 (2021) 1305–1314. doi: 10.1002/zaac.202100032.
[13] I. Polishuk and J. M. Garrido, “Comparison of SAFT-VR-Mie and CP-PC-SAFT in predicting phase behavior of associating systems IV. Methanol–aliphatic hydrocarbons,” Journal of Molecular Liquids, 291 (2019) 111321, Oct. 2019, doi: 10.1016/j.molliq.2019.111321.
[14] W. Hu, Z. Shang, N. Wei, B. Hou, J. Gong, and Y. Wang, “Solubility of benorilate in twelve monosolvents: Determination, correlation and COSMO-RS analysis,” The Journal of Chemical Thermodynamics, 152 (2021) 106272. doi: 10.1016/j.jct.2020.106272.
[15] S. Balchandani and R. Singh, “Thermodynamic analysis using COSMO-RS studies of reversible ionic liquid 3-aminopropyl triethoxysilane blended with amine activators for CO2 absorption,” Journal of Molecular Liquids, 324 (2021) 114713. doi: 10.1016/j.molliq.2020.114713.
[16] R. Xiong, S. I. Sandler, and R. I. Burnett, “An Improvement to COSMO-SAC for Predicting Thermodynamic Properties,” Ind. Eng. Chem. Res., 53 (2014) 8265–8278, May 2014, doi: 10.1021/ie404410v.
[17] S. Wang, S. I. Sandler, and C.-C. Chen, “Refinement of COSMO−SAC and the Applications,” Ind. Eng. Chem. Res., 46 (2007) 7275–7288. doi: 10.1021/ie070465z.
[18] R. Fingerhut et al., “Comprehensive Assessment of COSMO-SAC Models for Predictions of Fluid-Phase Equilibria,” Ind. Eng. Chem. Res., 56 (2017) 9868–9884. doi: 10.1021/acs.iecr.7b01360.
[19] A. De Visscher, J. Vanderdeelen, E. Königsberger, B. R. Churagulov, M. Ichikuni, and M. Tsurumi, “IUPAC-NIST Solubility Data Series. 95. Alkaline Earth Carbonates in Aqueous Systems. Part 1. Introduction, Be and Mg,” Journal of Physical and Chemical Reference Data, vol. 41 (2012) 013105-013105–67. doi: 10.1063/1.3675992.
[20] “ThermoLit: NIST Literature Report Builder for Thermochemical Property Measurements.” https://trc.nist.gov/thermolit/main/home.html#home (accessed Feb. 09, 2022).
[21] L. Goerigk, A. Hansen, C. Bauer, S. Ehrlich, A. Najibi, and S. Grimme, “A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions,” Phys. Chem. Chem. Phys., 19 (2017) 32184–32215. doi: 10.1039/C7CP04913G.
[22] R. Peverati and D. G. Truhlar, “Quest for a universal density functional: the accuracy of density functionals across a broad spectrum of databases in chemistry and physics,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 372 (2014) 20120476, doi: 10.1098/rsta.2012.0476.
[23] P. Morgante and R. Peverati, “ACCDB: A collection of chemistry databases for broad computational purposes,” Journal of Computational Chemistry, 40, (2019) 839–848. 10.1002/jcc.25761.
[24] D. L. Mobley and J. P. Guthrie, “FreeSolv: a database of experimental and calculated hydration free energies, with input files,” J Comput Aided Mol Des, 28 (2014) 711–720. doi: 10.1007/s10822-014-9747-x.
[25] S. Abaimov and M. Martellini, “Understanding Machine Learning,” in Machine Learning for Cyber Agents: Attack and Defence, S. Abaimov and M. Martellini, Eds., in Advanced Sciences and Technologies for Security Applications. Cham: Springer International Publishing, (2022) 15–89. doi: 10.1007/978-3-030-91585-8_2.
[26] A. L. Buczak and E. Guven, “A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection,” IEEE Communications Surveys Tutorials, 18 (2016) 1153–1176, doi: 10.1109/COMST.2015.2494502.
[27] A. Malloum, J. J. Fifen, and J. Conradie, “Determination of the absolute solvation free energy and enthalpy of the proton in solutions,” Journal of Molecular Liquids, 322 (2021) 114919. doi: 10.1016/j.molliq.2020.114919.
[28] J. Wang et al., “Hydration Energetics of a Diamine-Appended Metal–Organic Framework Carbon Capture Sorbent,” J. Phys. Chem. C, 124 (2020) 398–403. doi: 10.1021/acs.jpcc.9b08008.
[29] S. A. Potekhin, “High-Pressure Scanning Microcalorimetry – A New Method for Studying Conformational and Phase Transitions,” Biochemistry Moscow, 83 (2018) S134–S145, doi: 10.1134/S0006297918140110.
[30] M. Bonto, H. M. Nick, and A. A. Eftekhari, “Thermodynamic Analysis of the Temperature Effect on Calcite Surface Reactions in Aqueous Environments,” Energy Fuels, 35 (2021) 16677–16692, doi: 10.1021/acs.energyfuels.1c01652.
[31] D. Zheng and F. Wang, “Performing Molecular Dynamics Simulations and Computing Hydration Free Energies on the B3LYP-D3(BJ) Potential Energy Surface with Adaptive Force Matching: A Benchmark Study with Seven Alcohols and One Amine,” ACS Phys. Chem Au, 1 (2021) 14–24, doi: 10.1021/acsphyschemau.1c00006.
[32] T. R. Rogers and F. Wang, “Accurate MP2-based force fields predict hydration free energies for simple alkanes and alcohols in good agreement with experiments,” J. Chem. Phys., 153 (2020) 244505, doi: 10.1063/5.0035032.
[33] P. Sahu, S. Krishnaswamy, K. Ponnani, and N. K. Pande, “A thermodynamic approach to selection of suitable hydrate formers for seawater desalination,” Desalination, 436 (2018) 144–151, doi: 10.1016/j.desal.2018.02.001.
[34] I. H. Bell et al., “A Benchmark Open-Source Implementation of COSMO-SAC,” J. Chem. Theory Comput., 16 (2020) 2635–2646, doi: 10.1021/acs.jctc.9b01016.
[35] COSMO-SAC. National Institute of Standards and Technology, 2022. Accessed: Jul. 01, 2022. [Online]. Available: https://github.com/usnistgov/COSMOSAC
[36] M. Awad and R. Khanna, “Support Vector Regression,” in Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, M. Awad and R. Khanna, Eds., Berkeley, CA: (2015) 67–80. doi: 10.1007/978-1-4302-5990-9_4.
[37] J. VanderPlas, Python data science handbook: Essential tools for working with data. O’Reilly Media, Inc., (2016).
[38] R. Ramanathan, M. Mathirajan, and A. R. Ravindran, Eds., Big Data Analytics Using Multiple Criteria Decision-Making Models. Boca Raton: CRC Press, (2017) doi: 10.1201/9781315152653.
[39] T.-C. Liu and S.-T. Lin, “A new approach for developing exact local composition models for lattice fluids,” Journal of the Taiwan Institute of Chemical Engineers, 96 (2019) 63–73, doi: 10.1016/j.jtice.2018.11.023.
[40] S. Balchandani and R. Singh, “Thermodynamic analysis using COSMO-RS studies of reversible ionic liquid 3-aminopropyl triethoxysilane blended with amine activators for CO2 absorption,” Journal of Molecular Liquids, 324 (2021) 114713, doi: 10.1016/j.molliq.2020.114713.
[41] W. Hu, Z. Shang, N. Wei, B. Hou, J. Gong, and Y. Wang, “Solubility of benorilate in twelve monosolvents: Determination, correlation and COSMO-RS analysis,” The Journal of Chemical Thermodynamics, 152 (2021) 106272, doi: 10.1016/j.jct.2020.106272.
[42] M. R. Shah and G. D. Yadav, “Prediction of Liquid–Liquid Equilibria for Biofuel Applications by Quantum Chemical Calculations Using the Cosmo-SAC Method,” Ind. Eng. Chem. Res., 50 (2011) 13066–13075, doi: 10.1021/ie201454m.
