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.; Simmons, Blake A.; Singh, Seema; Kidder, Michelle K.; Smith, Jeremy C.

doi:10.1021/acssuschemeng.3c00624

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

Journal Article · 10 May 2023 · ACS Sustainable Chemistry & Engineering

DOI: https://doi.org/10.1021/acssuschemeng.3c00624 · OSTI ID:1974323

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^[2]; Demerdash, Omar N. ^[1];

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Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Molecular Biophysics
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Molecular Biophysics; University of Tennessee, Knoxville, TN (United States)
Joint BioEnergy Institute (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Joint BioEnergy Institute (JBEI), Emeryville, CA (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

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

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

Grant/Contract Number:: AC05-00OR22725; FWP ERKP752; AC02-05CH11231; 3ERKCG25

OSTI ID:: 1974323

Journal Information:: ACS Sustainable Chemistry & Engineering, Vol. 11, Issue 20; ISSN 2168-0485

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
ionic liquids
machine learning
surface tension
speed of sound
COSMO-RS
sigma profiles
gradient boosting tree

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

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