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Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents.

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Machine learning models accurately predict deep eutectic solvent (DES) viscosity, overcoming experimental limitations. This accelerates the design of eco-friendly DESs for industrial applications by enabling rapid property assessment.

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Computational Chemistry

Background:

  • Deep eutectic solvents (DESs) are promising eco-friendly solvents for industrial mass and heat transfer.
  • Predicting DES viscosity is challenging due to the vast number of possible formulations and environmental factors.
  • Experimental viscosity measurements for all potential DESs are infeasible, hindering their widespread application.

Purpose of the Study:

  • To develop accurate and rapid machine learning (ML) models for predicting DES viscosity.
  • To facilitate the rational design of DESs for specific industrial applications.
  • To provide a computational tool for understanding DES properties.

Main Methods:

  • Developed and compared three ML models: Support Vector Regression (SVR), Feed Forward Neural Networks (FFNNs), and Categorical Boosting (CatBoost).
  • Utilized a comprehensive dataset of over 670 DESs across a wide temperature range (278.15–385.25 K).
  • Employed Quantum chemistry-based, COSMO-RS-derived sigma profile (σ-profile) features as inputs for ML models.
  • Interpreted ML model predictions using SHapley Additive Explanation (SHAP) analysis.

Main Results:

  • The CatBoost model demonstrated excellent predictive performance with R² of 0.99, low RMSE, and AARD of 5.22% on external test sets.
  • 98% of data points fell within 15% of average absolute relative deviations, indicating high accuracy.
  • ML models significantly outperformed baseline regression methods (multilinear and two-factor polynomial regression).

Conclusions:

  • Machine learning models, particularly CatBoost, provide accurate and rapid prediction of DES viscosity.
  • These models, informed by quantum chemistry features, can accelerate the discovery and design of novel DESs.
  • The developed approach aids in overcoming experimental limitations and promotes the industrial adoption of designer solvents.