Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids
暂无分享,去创建一个
Jeremy C. Smith | B. Simmons | Seema Singh | M. Kidder | MicholasDean Smith | Omar N. A. Demerdash | Mood Mohan
[1] R. Gardas,et al. Influence of alkyl substituent on thermophysical properties and CO2 absorption studies of diethylenetriamine- based ionic liquids , 2023, Journal of Molecular Liquids.
[2] B. Simmons,et al. Multiscale molecular simulations for the solvation of lignin in ionic liquids , 2023, Scientific Reports.
[3] Honglai Liu,et al. Predicting the Self-Diffusion Coefficient of Liquids Based on Backpropagation Artificial Neural Network: A Quantitative Structure–Property Relationship Study , 2022, Industrial & Engineering Chemistry Research.
[4] T. Karakasidis,et al. The Electrical Conductivity of Ionic Liquids: Numerical and Analytical Machine Learning Approaches , 2022, Fluids.
[5] Salema K. Hadrawi,et al. Prediction of speed of sound and specific heat capacity of ionic liquids using predictive SAFT-based equation of state , 2022, Chemical Engineering Science.
[6] Saleh A. Ahmed,et al. Novel and accurate mathematical simulation of various models for accurate prediction of surface tension parameters through ionic liquids , 2022, Arabian Journal of Chemistry.
[7] Honglai Liu,et al. Predicting the Thermal Conductivity of Ionic Liquids Using a Quantitative Structure–Property Relationship , 2022, Industrial & Engineering Chemistry Research.
[8] Ke-Jun Wu,et al. Transition State Theory-Inspired Neural Network for Estimating the Viscosity of Deep Eutectic Solvents , 2022, ACS central science.
[9] U. Rathnayake,et al. A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP) , 2022, Case Studies in Construction Materials.
[10] Yamil J. Colón,et al. Sigma profiles in deep learning: towards a universal molecular descriptor. , 2022, Chemical communications.
[11] D. Niu,et al. Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm , 2021, Sustainability.
[12] Lanjing Zhang,et al. Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models , 2021, Laboratory Investigation.
[13] B. Simmons,et al. Integration of acetic acid catalysis with one-pot protic ionic liquid configuration to achieve high-efficient biorefinery of poplar biomass , 2021, Green Chemistry.
[14] Lifang Chen,et al. Prediction of CO2 solubility in deep eutectic solvents using random forest model based on COSMO-RS-derived descriptors , 2021, Green Chemical Engineering.
[15] J. Coutinho,et al. Using COSMO-RS to Predict Solvatochromic Parameters for Deep Eutectic Solvents , 2021, ACS Sustainable Chemistry & Engineering.
[16] Y. Ok,et al. Applied Machine Learning for Prediction of CO2 Adsorption on Biomass Waste-Derived Porous Carbons. , 2021, Environmental science & technology.
[17] Abdolhossein Hemmati-Sarapardeh,et al. Modeling surface tension of ionic liquids by chemical structure-intelligence based models , 2021, Journal of Molecular Liquids.
[18] B. Hartke,et al. Improved prediction of solvation free energies by machine-learning polarizable continuum solvation model , 2021, Nature Communications.
[19] W. Y. Tey,et al. Combination of ultrasonication and deep eutectic solvent in pretreatment of lignocellulosic biomass for enhanced enzymatic saccharification , 2021, Cellulose.
[20] J. Pleiss,et al. Analysis of Thermophysical Properties of Deep Eutectic Solvents by Data Integration , 2019, Journal of Chemical & Engineering Data.
[21] R. Biczak,et al. Phytotoxicity of ionic liquids. , 2019, Chemosphere.
[22] Xiaoyan Ji,et al. Modeling Thermodynamic Derivative Properties and Gas Solubility of Ionic Liquids with ePC-SAFT , 2019, Industrial & Engineering Chemistry Research.
[23] V. Goud,et al. COSMO-RS-Based Screening of Antisolvents for the Separation of Sugars from Ionic Liquids: Experimental and Molecular Dynamic Simulations , 2018, ACS omega.
[24] G. Járvás,et al. A novel method for the surface tension estimation of ionic liquids based on COSMO-RS theory , 2018, Fluid Phase Equilibria.
[25] M. Dzida,et al. Isobaric and Isochoric Heat Capacities as Well as Isentropic and Isothermal Compressibilities of Di- and Trisubstituted Imidazolium-Based Ionic Liquids as a Function of Temperature , 2018 .
[26] Sandip Paul,et al. Solubility of glucose in tetrabutylammonium bromide based deep eutectic solvents: Experimental and molecular dynamic simulations , 2017 .
[27] D. Tuli,et al. Ionic liquid pretreatment of biomass for sugars production: Driving factors with a plausible mechanism for higher enzymatic digestibility. , 2016, Carbohydrate polymers.
[28] V. Goud,et al. Effect of Protic and Aprotic Solvents on the Mechanism of Cellulose Dissolution in Ionic Liquids: A Combined Molecular Dynamics and Experimental Insight , 2016 .
[29] V. Goud,et al. Solid Liquid Equilibrium of Cellobiose, Sucrose, and Maltose Monohydrate in Ionic Liquids: Experimental and Quantum Chemical Insights , 2016 .
[30] Rolf E. Isele-Holder,et al. Predicting Octanol/Water Partition Coefficients of Alcohol Ethoxylate Surfactants Using a Combination of Molecular Dynamics and the Conductor-like Screening Model for Realistic Solvents , 2016 .
[31] A. Bahadori,et al. Prediction of the binary surface tension of mixtures containing ionic liquids using Support Vector Machine algorithms , 2015 .
[32] R. Atkin,et al. Structure and nanostructure in ionic liquids. , 2015, Chemical reviews.
[33] G. Pazuki,et al. Modeling of surface tension for ionic liquids using group method of data handling , 2015, Ionics.
[34] Yiying Jin,et al. Effects of thermal pretreatment on acidification phase during two-phase batch anaerobic digestion of kitchen waste , 2015 .
[35] V. Goud,et al. Thermodynamic Insights in the Separation of Cellulose/Hemicellulose Components from Lignocellulosic Biomass Using Ionic Liquids , 2015, Journal of Solution Chemistry.
[36] G. Járvás,et al. Temperature dependent surface tension estimation using COSMO-RS sigma moments , 2014 .
[37] F. Gharagheizi,et al. Determination of the speed of sound in ionic liquids using a least squares support vector machine group contribution method , 2014 .
[38] Chaohong He,et al. Speed of sound of ionic liquids: Database, estimation, and its application for thermal conductivity prediction , 2014 .
[39] Haifeng Dong,et al. A new fragment contribution‐corresponding states method for physicochemical properties prediction of ionic liquids , 2013 .
[40] F. Gharagheizi,et al. Group contribution model for estimation of surface tension of ionic liquids , 2012 .
[41] Marcus D. Hanwell,et al. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform , 2012, Journal of Cheminformatics.
[42] A. Klamt. The COSMO and COSMO‐RS solvation models , 2011 .
[43] A. P. Serro,et al. High-temperature surface tension and density measurements of 1-alkyl-3-methylimidazolium bistriflamide ionic liquids , 2010 .
[44] Morteza Zare,et al. Temperature dependence of viscosity and relation with the surface tension of ionic liquids , 2010 .
[45] Andreas Klamt,et al. Prediction of the free energy of hydration of a challenging set of pesticide-like compounds. , 2009, The journal of physical chemistry. B.
[46] A. P. Serro,et al. Viscosity and Surface Tension of 1-Ethanol-3-methylimidazolium Tetrafluoroborate and 1-Methyl-3-octylimidazolium Tetrafluoroborate over a Wide Temperature Range , 2009 .
[47] João A. P. Coutinho,et al. Estimation of speed of sound of ionic liquids using surface tensions and densities: A volume based approach , 2008 .
[48] J. Coutinho,et al. Applying a QSPR correlation to the prediction of surface tensions of ionic liquids , 2008 .
[49] Paul Scovazzo,et al. Correlations of Low-Pressure Carbon Dioxide and Hydrocarbon Solubilities in Imidazolium-, Phosphonium-, and Ammonium-Based Room-Temperature Ionic Liquids. Part 2. Using Activation Energy of Viscosity , 2008 .
[50] K. R. Seddon,et al. Density, Speed of Sound, and Derived Thermodynamic Properties of Ionic Liquids over an Extended Pressure Range. 4. [C3mim][NTf2] and [C5mim][NTf2] , 2006 .
[51] G. Keglevich,et al. Application of ionic liquids in palladium(II) catalyzed homogenous transfer hydrogenation , 2005 .
[52] I. Marrucho,et al. Corresponding-States Modeling of the Speed of Sound of Long-Chain Hydrocarbons , 2005 .
[53] A. F. Estrada-Alexanders,et al. New method for deriving accurate thermodynamic properties from speed-of-sound , 2004 .
[54] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[55] A. Klamt,et al. Fast solvent screening via quantum chemistry: COSMO‐RS approach , 2002 .
[56] K. Kjaer,et al. Design and characterization of crystalline thin film architectures at the air-liquid interface: simplicity to complexity. , 2001, Chemical reviews.
[57] R. Sheldon,et al. Lipase-catalyzed reactions in ionic liquids. , 2000, Organic letters.
[58] Tom Welton,et al. Room-temperature ionic liquids: solvents for synthesis and catalysis. 2. , 1999, Chemical reviews.
[59] Ilan Benjamin,et al. Chemical Reactions and Solvation at Liquid Interfaces: A Microscopic Perspective. , 1996, Chemical reviews.
[60] J. Keasling,et al. In silico COSMO-RS predictive screening of ionic liquids for the dissolution of plastic , 2022, Green Chemistry.
[61] Wengang Zhang,et al. Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization , 2021 .
[62] S. Raeissi,et al. Simple estimations of the speed of sound in ionic liquids, with and without any physical property data available , 2020 .
[63] M. Sierka,et al. Turbomole , 2014 .
[64] Jian-ying Wang,et al. Thermophysical properties of 1-methyl-3-methylimidazolium dimethylphosphate and 1-ethyl-3-methylimidazolium diethylphosphate , 2011 .
[65] I. Langmuir. Forces Near the Surfaces of Molecules. , 1930 .