High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning

Mohan, Mood; Jetti, Karuna Devi; Guggilam, Sreelekha; Smith, Micholas Dean; Kidder, Michelle K.; Smith, Jeremy C.

doi:10.1021/acssuschemeng.4c00631

Title: High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning

Journal Article · 23 April 2024 · ACS Sustainable Chemistry & Engineering

DOI: https://doi.org/10.1021/acssuschemeng.4c00631 · OSTI ID:2345312

^[1]; Jetti, Karuna Devi ^[2]; Guggilam, Sreelekha ^[1];

^[3];

^[1];

^[3]

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
GITAM, Vishakhapatnam (India)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)

Ionic liquids (ILs) are a novel group of green solvents with great promise for various industrial applications, including carbon capture and lignocellulosic biomass deconstruction. However, the use of ILs at the industrial scale remains challenging due to their high viscosities at ambient temperatures. To develop ILs with lower viscosities, a systematic study of their quantitative structure–property relationship (QSPR) is desirable. Here, we developed four machine learning (ML) models to predict viscosity at various temperature and pressure ranges, trained over a wide range of ILs consisting of various cationic and anionic families. ML methods including two-factor polynomial regression (two-factor PR), support vector regression (SVR), feed-forward neural networks (FFNN), and categorical boosting (CATBoost) were developed based on features that have proven useful in previous ML studies: COSMO-RS (conductor-like screening model for real solvents)-derived surface screening charge densities (sigma profiles). FFNN and CATBoost were the most accurate in predicting IL viscosities with lower average absolute relative deviation and higher R2 values on the test set. Tanimoto similarity scores were calculated to characterize the chemical space and structural similarity of the investigated ions. Furthermore, SHapley Additive exPlanation (SHAP) analysis was employed to interpret the ML results. Temperature, the polar area of ILs, and the nonpolar regions of ions are key features that influence the viscosity predictions. Importantly, the IL viscosity prediction here is the most accurate reported to date.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB)

Grant/Contract Number:: AC05-00OR22725; SC0022214

OSTI ID:: 2345312

Journal Information:: ACS Sustainable Chemistry & Engineering, Vol. 12, Issue 18; 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
viscosity
machine learning
sigma profiles
CATBoost model
SHAP analysis
molecular SMILES

Title: High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning

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