Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents

Mohan, Mood; Demerdash, Omar N.; Simmons, Blake A.; Singh, Seema; Kidder, Michelle K.; Smith, Jeremy C.

doi:10.1021/acsomega.4c01175

Title: Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents

Journal Article · Fri Apr 19 00:00:00 UTC 2024 · ACS Omega

DOI: https://doi.org/10.1021/acsomega.4c01175 · OSTI ID:2339676

^[1]; Demerdash, Omar N. ^[1];

^[2]; Singh, Seema ^[3];

^[4];

^[5]

Biosciences Division and Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
Deconstruction Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, California 94608, United States, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, United States
Deconstruction Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, California 94608, United States
Manufacturing Science Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6201, United States
Biosciences Division and Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States, Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee 37996, United States

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB); USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231; AC05-00OR22725; SC0022214; SC0022273

OSTI ID:: 2339676

Journal Information:: ACS Omega, Journal Name: ACS Omega Journal Issue: 17 Vol. 9; ISSN 2470-1343

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

References (56)

The COSMO and COSMO-RS solvation models: The COSMO and COSMO-RS solvation models Klamt, Andreas Wiley Interdisciplinary Reviews: Computational Molecular Science, Vol. 1, Issue 5 https://doi.org/10.1002/wcms.56	journal	April 2011
Machine learning prediction of lignin content in poplar with Raman spectroscopy Gao, Wenli; Zhou, Liang; Liu, Shengquan Bioresource Technology, Vol. 348 https://doi.org/10.1016/j.biortech.2022.126812	journal	March 2022
Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy Gao, Wenli; Zhou, Liang; Liu, Shengquan Carbohydrate Polymers, Vol. 292 https://doi.org/10.1016/j.carbpol.2022.119635	journal	September 2022
An improved machine learning approach to estimate hemicellulose, cellulose, and lignin in biomass Kartal, Furkan; Özveren, Uğur Carbohydrate Polymer Technologies and Applications, Vol. 2 https://doi.org/10.1016/j.carpta.2021.100148	journal	December 2021
Investigation of CO2 solubility in monoethanolamine hydrochloride based deep eutectic solvents and physical properties measurements Pishro, Khatereh Ali; Murshid, Ghulam; Mjalli, Farouq Sabri Chinese Journal of Chemical Engineering, Vol. 28, Issue 11 https://doi.org/10.1016/j.cjche.2020.07.004	journal	November 2020
Similarity-based virtual screening using 2D fingerprints Willett, Peter Drug Discovery Today, Vol. 11, Issue 23-24 https://doi.org/10.1016/j.drudis.2006.10.005	journal	December 2006
Solubility of glucose in tetrabutylammonium bromide based deep eutectic solvents: Experimental and molecular dynamic simulations Mohan, Mood; Naik, Papu Kumar; Banerjee, Tamal Fluid Phase Equilibria, Vol. 448 https://doi.org/10.1016/j.fluid.2017.05.024	journal	September 2017
Investigation of glycerol-derived binary and ternary systems in CO2 capture process Huang, Zhaohe; Jiang, Bin; Yang, Huawei Fuel, Vol. 210 https://doi.org/10.1016/j.fuel.2017.08.043	journal	December 2017
Prediction of CO2 solubility in deep eutectic solvents using random forest model based on COSMO-RS-derived descriptors Wang, Jingwen; Song, Zhen; Chen, Lifang Green Chemical Engineering, Vol. 2, Issue 4 https://doi.org/10.1016/j.gce.2021.08.002	journal	December 2021
Ionic liquids/deep eutectic solvents for CO2 capture: Reviewing and evaluating Liu, Yanrong; Dai, Zhengxing; Zhang, Zhibo Green Energy & Environment, Vol. 6, Issue 3 https://doi.org/10.1016/j.gee.2020.11.024	journal	June 2021
Exploiting the hydrophilic role of natural deep eutectic solvents for greening CO2 capture Ren, Hongwei; Lian, Shaohan; Wang, Xue Journal of Cleaner Production, Vol. 193 https://doi.org/10.1016/j.jclepro.2018.05.051	journal	August 2018
Advancements in carbon capture technologies: A review Dubey, Aseem; Arora, Akhilesh Journal of Cleaner Production, Vol. 373 https://doi.org/10.1016/j.jclepro.2022.133932	journal	November 2022
Basicity-controlled DBN-based deep eutectic solvents for efficient carbon dioxide capture Sang, Haina; Su, Li; Han, Weifang Journal of CO2 Utilization, Vol. 65 https://doi.org/10.1016/j.jcou.2022.102201	journal	November 2022
Solubilities and thermodynamic properties of CO2 in choline-chloride based deep eutectic solvents Li, Guihua; Deng, Dongshun; Chen, Yanfei The Journal of Chemical Thermodynamics, Vol. 75 https://doi.org/10.1016/j.jct.2014.04.012	journal	August 2014
Effect of water or ethanol on the excess properties of deep eutectic solvents (tetrabutylammonium bromide + formic acid/propionic acid) Zuo, Yingying; Chen, Xiguang; Wei, Ning Journal of Molecular Liquids, Vol. 383 https://doi.org/10.1016/j.molliq.2023.122034	journal	August 2023
Insights into the physicochemical properties of newly synthesized benzyl triethylammonium chloride-based deep eutectic solvents Singh, Kuldeep; Shibu, Reshma P.; Mehra, Sanjay Journal of Molecular Liquids, Vol. 386 https://doi.org/10.1016/j.molliq.2023.122589	journal	September 2023
Deep Eutectic Solvents: A Review of Fundamentals and Applications Hansen, Benworth B.; Spittle, Stephanie; Chen, Brian Chemical Reviews, Vol. 121, Issue 3 https://doi.org/10.1021/acs.chemrev.0c00385	journal	December 2020
Superbase/Acylamido-Based Deep Eutectic Solvents for Multiple-Site Efficient CO ₂ Absorption Jiang, Bin; Ma, Jingwen; Yang, Na Energy & Fuels, Vol. 33, Issue 8 https://doi.org/10.1021/acs.energyfuels.9b01361	journal	July 2019
Gas Solubility Using Deep Eutectic Solvents: Review and Analysis Pelaquim, Fernanda Paludetto; Barbosa Neto, Antonio Marinho; Dalmolin, Irede Angela Lucini Industrial & Engineering Chemistry Research, Vol. 60, Issue 24 https://doi.org/10.1021/acs.iecr.1c00947	journal	June 2021
Carbon Dioxide Solubility in Phosphonium-Based Deep Eutectic Solvents: An Experimental and Molecular Dynamics Study Wang, Jingwen; Cheng, Hongye; Song, Zhen Industrial & Engineering Chemistry Research, Vol. 58, Issue 37 https://doi.org/10.1021/acs.iecr.9b03740	journal	August 2019
Solubilities and Thermodynamic Properties of Carbon Dioxide in Guaiacol-Based Deep Eutectic Solvents Liu, Xiaobang; Gao, Bao; Jiang, Yaotai Journal of Chemical & Engineering Data, Vol. 62, Issue 4 https://doi.org/10.1021/acs.jced.6b01013	journal	March 2017
Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents Mohan, Mood; Jetti, Karuna Devi; Smith, Micholas Dean Journal of Chemical Theory and Computation https://doi.org/10.1021/acs.jctc.3c01163	journal	February 2024
Molecular Dynamic Simulations for the Extraction of Quinoline from Heptane in the Presence of a Low-Cost Phosphonium-Based Deep Eutectic Solvent Naik, Papu Kumar; Mohan, Mood; Banerjee, Tamal The Journal of Physical Chemistry B, Vol. 122, Issue 14 https://doi.org/10.1021/acs.jpcb.7b10914	journal	March 2018
Transition State Theory-Inspired Neural Network for Estimating the Viscosity of Deep Eutectic Solvents Yu, Liu-Ying; Ren, Gao-Peng; Hou, Xiao-Jing ACS Central Science, Vol. 8, Issue 7 https://doi.org/10.1021/acscentsci.2c00157	journal	July 2022
Predicting the CO₂ Capture Capability of Deep Eutectic Solvents and Screening over 1000 of their Combinations Using Machine Learning Lemaoui, Tarek; Boublia, Abir; Lemaoui, Soumaya ACS Sustainable Chemistry & Engineering, Vol. 11, Issue 26 https://doi.org/10.1021/acssuschemeng.3c00415	journal	June 2023
Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids Mohan, Mood; Smith, Micholas Dean; Demerdash, Omar N. ACS Sustainable Chemistry & Engineering, Vol. 11, Issue 20 https://doi.org/10.1021/acssuschemeng.3c00624	journal	May 2023
Novel Strategy of Machine Learning for Predicting Henry’s Law Constants of CO₂ in Ionic Liquids Zhang, Wanxiang; Wang, Yan; Ren, Shuhang ACS Sustainable Chemistry & Engineering, Vol. 11, Issue 15 https://doi.org/10.1021/acssuschemeng.3c00874	journal	April 2023
Operational Strategies and Comprehensive Evaluation of Menthol Based Deep Eutectic Solvent for the Extraction of Lower Alcohols from Aqueous Media Verma, Rupesh; Mohan, Mood; Goud, Vaibhav V. ACS Sustainable Chemistry & Engineering, Vol. 6, Issue 12 https://doi.org/10.1021/acssuschemeng.8b04255	journal	November 2018
Superbase Ionic Liquid-Based Deep Eutectic Solvents for Improving CO ₂ Absorption Yan, Hong; Zhao, Lei; Bai, Yinge ACS Sustainable Chemistry & Engineering, Vol. 8, Issue 6 https://doi.org/10.1021/acssuschemeng.9b07128	journal	January 2020
Combination of Fingerprint-Based Similarity Coefficients Using Data Fusion Salim, Naomie; Holliday, John; Willett, Peter Journal of Chemical Information and Computer Sciences, Vol. 43, Issue 2 https://doi.org/10.1021/ci025596j	journal	December 2002
chemicalize.orgchemicalize.orgby ChemAxon Ltd. Swain, Matthew Journal of Chemical Information and Modeling, Vol. 52, Issue 2 https://doi.org/10.1021/ci300046g	journal	January 2012
Unsupervised Data Base Clustering Based on Daylight's Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets Butina, Darko Journal of Chemical Information and Computer Sciences, Vol. 39, Issue 4 https://doi.org/10.1021/ci9803381	journal	July 1999
Deep Eutectic Solvents (DESs) and Their Applications Smith, Emma L.; Abbott, Andrew P.; Ryder, Karl S. Chemical Reviews, Vol. 114, Issue 21 https://doi.org/10.1021/cr300162p	journal	October 2014
Solubilities of Carbon Dioxide in Eutectic Mixtures of Choline Chloride and Dihydric Alcohols Chen, Yanfei; Ai, Ning; Li, Guihua Journal of Chemical & Engineering Data, Vol. 59, Issue 4 https://doi.org/10.1021/je400884v	journal	March 2014
Random Forests Breiman, Leo Machine Learning, Vol. 45, Issue 1, p. 5-32 https://doi.org/10.1023/A:1010933404324	journal	January 2001
Least Squares Support Vector Machine Classifiers Suykens, J. A. K.; Vandewalle, J. Neural Processing Letters, Vol. 9, Issue 3, p. 293-300 https://doi.org/10.1023/A:1018628609742	journal	June 1999
A quantum-chemical-based guide to analyze/quantify the cytotoxicity of ionic liquids Torrecilla, J. S.; Palomar, J.; Lemus, J. Green Chem., Vol. 12, Issue 1 https://doi.org/10.1039/B919806G	journal	January 2010
Anhydrous tertiary alkanolamines as hybrid chemical and physical CO ₂ capture reagents with pressure-swing regeneration Rainbolt, James E.; Koech, Phillip K.; Yonker, Clement R. Energy Environ. Sci., Vol. 4, Issue 2 https://doi.org/10.1039/C0EE00506A	journal	January 2011
Integration of polymerized ionic liquid with graphene for enhanced CO ₂ adsorption Tamilarasan, P.; Ramaprabhu, S. Journal of Materials Chemistry A, Vol. 3, Issue 1 https://doi.org/10.1039/C4TA04808C	journal	January 2015
CO₂ capture in ionic liquid 1-alkyl-3-methylimidazolium acetate: a concerted mechanism without carbene Yan, Fangyong; Dhumal, Nilesh R.; Kim, Hyung J. Physical Chemistry Chemical Physics, Vol. 19, Issue 2 https://doi.org/10.1039/C6CP06556B	journal	January 2017
Molecular mechanism of CO₂ absorption in phosphonium amino acid ionic liquid Prakash, Prabhat; Venkatnathan, Arun RSC Advances, Vol. 6, Issue 60 https://doi.org/10.1039/C6RA09577A	journal	January 2016
Industrial carbon dioxide capture and utilization: state of the art and future challenges Gao, Wanlin; Liang, Shuyu; Wang, Rujie Chemical Society Reviews, Vol. 49, Issue 23 https://doi.org/10.1039/D0CS00025F	journal	January 2020
Deep eutectic solvents composed of bio-phenol-derived superbase ionic liquids and ethylene glycol for CO₂ capture Wang, Ze; Wang, Zonghua; Huang, Xin Chemical Communications, Vol. 58, Issue 13 https://doi.org/10.1039/D1CC06856C	journal	January 2022
Sigma profiles in deep learning: towards a universal molecular descriptor Abranches, Dinis O.; Zhang, Yong; Maginn, Edward J. Chemical Communications, Vol. 58, Issue 37 https://doi.org/10.1039/D2CC01549H	journal	January 2022
CO₂ capture by 1,2,3-triazole-based deep eutectic solvents: the unexpected role of hydrogen bonds Wang, Zonghua; Wu, Congyi; Wang, Ze Chemical Communications, Vol. 58, Issue 53 https://doi.org/10.1039/D2CC02503E	journal	January 2022
Novel solvent properties of choline chloride/urea mixturesElectronic supplementary information (ESI) available: spectroscopic data. See http://www.rsc.org/suppdata/cc/b2/b210714g/ Abbott, Andrew P.; Capper, Glen; Davies, David L. Chemical Communications, Issue 1 https://doi.org/10.1039/b210714g	journal	November 2002
Carbon capture with ionic liquids: overview and progress Zhang, Xiangping; Zhang, Xiaochun; Dong, Haifeng Energy & Environmental Science, Vol. 5, Issue 5 https://doi.org/10.1039/c2ee21152a	journal	January 2012
In silico COSMO-RS predictive screening of ionic liquids for the dissolution of plastic Mohan, Mood; Keasling, Jay D.; Simmons, Blake A. Green Chemistry, Vol. 24, Issue 10 https://doi.org/10.1039/d1gc03464b	journal	January 2022
Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network Mohan, Mood; Demerdash, Omar; Simmons, Blake A. Green Chemistry, Vol. 25, Issue 9 https://doi.org/10.1039/d2gc04425k	journal	January 2023
Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning Mohan, Mood; Smith, Micholas Dean; Demerdash, Omar The Journal of Chemical Physics, Vol. 158, Issue 21 https://doi.org/10.1063/5.0147052	journal	June 2023
Machine learning prediction of the conversion of lignocellulosic biomass during hydrothermal carbonization Kardani, Navid; Hedayati Marzbali, Mojtaba; Shah, Kalpit Biofuels, Vol. 13, Issue 6 https://doi.org/10.1080/17597269.2021.1894780	journal	March 2021
Screening Deep Eutectic Solvents for CO2 Capture With COSMO-RS Liu, Yanrong; Yu, Hang; Sun, Yunhao Frontiers in Chemistry, Vol. 8 https://doi.org/10.3389/fchem.2020.00082	journal	February 2020
Carbon Dioxide Solubility in Nonionic Deep Eutectic Solvents Containing Phenolic Alcohols Alhadid, Ahmad; Safarov, Javid; Mokrushina, Liudmila Frontiers in Chemistry, Vol. 10 https://doi.org/10.3389/fchem.2022.864663	journal	March 2022
The Impact of Climate Change on Mental Health: A Systematic Descriptive Review Cianconi, Paolo; Betrò, Sophia; Janiri, Luigi Frontiers in Psychiatry, Vol. 11 https://doi.org/10.3389/fpsyt.2020.00074	journal	March 2020
CO2 Absorption Mechanism by the Deep Eutectic Solvents Formed by Monoethanolamine-Based Protic Ionic Liquid and Ethylene Glycol Cheng, Jinyu; Wu, Congyi; Gao, Weiji International Journal of Molecular Sciences, Vol. 23, Issue 3 https://doi.org/10.3390/ijms23031893	journal	February 2022
Study of Superbase-Based Deep Eutectic Solvents as the Catalyst in the Chemical Fixation of CO2 into Cyclic Carbonates under Mild Conditions García-Argüelles, Sara; Ferrer, Maria; Iglesias, Marta Materials, Vol. 10, Issue 7 https://doi.org/10.3390/ma10070759	journal	July 2017

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Title: Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents

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