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Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids

  • Mood Mohan*
    Mood Mohan
    Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
    Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
    *Email: [email protected]
    More by Mood Mohan
  • Micholas Dean Smith
    Micholas Dean Smith
    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
  • Omar N. Demerdash
    Omar N. Demerdash
    Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
    Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
  • Blake A. Simmons
    Blake A. Simmons
    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
  • Seema Singh
    Seema Singh
    Deconstruction Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, California 94608, United States
    More by Seema Singh
  • Michelle K. Kidder
    Michelle K. Kidder
    Manufacturing Science Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6201, United States
  • , and 
  • Jeremy C. Smith*
    Jeremy C. Smith
    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
    *Email: [email protected]
Cite this: ACS Sustainable Chem. Eng. 2023, 11, 20, 7809–7821
Publication Date (Web):May 10, 2023
https://doi.org/10.1021/acssuschemeng.3c00624
Copyright © 2023 American Chemical Society

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    Abstract

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    Ionic liquids (ILs) have unique solvent properties and have thus garnered significant interest. However, exhaustive experimental determination of the physicochemical properties of ILs is unrealistic due to the large structural diversity of anions and cations, their high cost, the requirements of elevated temperature and pressure, and the time required. To circumvent these experimental costs, computational approaches to accurately calculate these properties have emerged. In the present study, we present a demonstration of two machine learning (ML) models for the prediction of two critical IL physical properties, the surface tension and the speed of sound, across a wide range of temperatures and pressures. The models make use of molecular descriptors derived from the COSMO-RS, a quantum chemical-based model. The ML models show excellent agreement with experimental observations, with an R2 value of 0.96–0.99 and RMSE of 1.71 mN/m and 16.12 m/s for the surface tension and speed of sound, respectively. This work paves the way for the development of COSMO-RS-informed ML models for the prediction of IL properties which can help to further optimize and accelerate technology development for ILs.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acssuschemeng.3c00624.

    • The IL property data in different ILs at different experimental conditions along with different ML model predictions and the list of anions and cations (PDF)

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    Cited By

    This article is cited by 4 publications.

    1. Mood Mohan, Karuna Devi Jetti, Micholas Dean Smith, Omar N. Demerdash, Michelle K. Kidder, Jeremy C. Smith. Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents. Journal of Chemical Theory and Computation 2024, 20 (9) , 3911-3926. https://doi.org/10.1021/acs.jctc.3c01163
    2. Mood Mohan, Karuna Devi Jetti, Sreelekha Guggilam, Micholas Dean Smith, Michelle K. Kidder, Jeremy C. Smith. High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning. ACS Sustainable Chemistry & Engineering 2024, 12 (18) , 7040-7054. https://doi.org/10.1021/acssuschemeng.4c00631
    3. Mood Mohan, Omar N. Demerdash, Blake A. Simmons, Seema Singh, Michelle K. Kidder, Jeremy C. Smith. Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents. ACS Omega 2024, 9 (17) , 19548-19559. https://doi.org/10.1021/acsomega.4c01175
    4. Xiao Liu, Yu Gu, Mengxian Yu, Qingzhu Jia, Yin-Ning Zhou, Fangyou Yan, Qiang Wang. Norm Indices-Driven Robust QSPR Model for Mining Temperature-Dependent Properties of Ionic Liquids. ACS Sustainable Chemistry & Engineering 2023, 11 (36) , 13429-13440. https://doi.org/10.1021/acssuschemeng.3c03436