High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning Article Swipe
Mood Mohan
,
Karuna Devi Jetti
,
Sreelekha Guggilam
,
Micholas Dean Smith
,
Michelle K. Kidder
,
Jeremy C. Smith
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1021/acssuschemeng.4c00631
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1021/acssuschemeng.4c00631
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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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acssuschemeng.4c00631
- OA Status
- green
- Cited By
- 21
- References
- 89
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4395026338
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4395026338Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1021/acssuschemeng.4c00631Digital Object Identifier
- Title
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High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-23Full publication date if available
- Authors
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Mood Mohan, Karuna Devi Jetti, Sreelekha Guggilam, Micholas Dean Smith, Michelle K. Kidder, Jeremy C. SmithList of authors in order
- Landing page
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https://doi.org/10.1021/acssuschemeng.4c00631Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.osti.gov/servlets/purl/2345312Direct OA link when available
- Concepts
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Ionic liquid, Viscosity, Quantitative structure–activity relationship, Linear regression, Machine learning, Test set, Support vector machine, Artificial intelligence, Chemistry, Biological system, Computer science, Materials science, Organic chemistry, Catalysis, Composite material, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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21Total citation count in OpenAlex
- Citations by year (recent)
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2025: 16, 2024: 5Per-year citation counts (last 5 years)
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89Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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