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July 12, 2026Energy & Fuels

Prediction of Hydrogen Solubility in Ionic Liquids by Machine Learning Models Based on COSMO-RS Descriptors

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Authors

ZYZhengsi YinHCH S ChenYTYuan Tian

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Overview

Randomized trial predicts hydrogen solubility in ionic liquids, suggesting efficient screening methods for applications.

Key Points

  • This study aims to predict hydrogen solubility in ionic liquids using machine learning models based on COSMO-RS descriptors.
  • Four machine learning models (GBR, XGBoost, LightGBM, MLP) were developed.
  • Conductor-like screening model (COSMO-RS) was used to obtain σ-profiles as feature descriptors.
  • Models were validated through 5-fold cross validation and Bayesian hyperparameter optimization.
  • The MLP model achieved R² of 0.9928 and RMSE of 0.00629 on the test set.
  • Predictions on an external independent validation set aligned well with experimental values.
  • Analysis reveals the significant impact of σ-profiles on hydrogen dissolution behavior.

Cite This Study

Yin et al. (2026) studied this question.

synapsesocial.com/papers/6a532e014f7abc118adeca2dhttps://doi.org/10.1021/acs.energyfuels.6c02266
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