Scholarlink

<p>The study presents a predictive approach for the rheological properties of ionogels (IGs), i.e. storage modulus (<i>G’</i>) and loss modulus (<i>G”)</i>, using molecular descriptors and Artificial Neural Network (ANN). A total of 5,670 molecular descriptors, generated via alvaDesc and weighted by mole fraction, serve as input parameters, while experimental <i>G’</i> and <i>G”</i> values sourced from the literature used as outputs. The collected data are divided into training, validation, and testing sets for ANN model development. Hyperparameter optimization using a grid search with the Levenberg-Marquardt algorithm (LMA) achieved a coefficient of determination (<i>R²)</i> of 0.88 and mean squared error (<i>MSE)</i> of 6.9 × 10<sup>− 9</sup> for 50% reduced Normalised dataset (60/20/20 split, 5 hidden layers, 0.005 learning rate, 32 batch size, 100 epochs). Scaled Conjugate Gradient (SCG) algorithm is further used to improve emission prediction of ANN model to <i>R</i><sup><i>2</i></sup> of 0.96 and <i>MSE</i> of 2.5 × 10<sup>− 9</sup> using a network architecture with 3 hidden layers containing 25 neurons each, trained over 200 epochs. In classification tasks, the Terpyridine-3-butylimidazolium salt (Tpy) gelator exhibited the best performance for <i>G’</i> and <i>G”</i> (<i>R²</i> = 0.98 and 0.95). For hydrophobic ionic liquids (ILs), the perfluorobutanesulfonate (C<sub>4</sub>F<sub>9</sub>SO<sub>3</sub>) group achieved <i>R²</i> of 0.98 for both moduli, while for hydrophilic ILs, (bis(trifluoromethylsulfonyl)amide (TFSA) systems showed <i>R²</i> of 0.96 (<i>G’)</i> and 0.98 (<i>G”).</i> These findings demonstrate the potential of descriptor-based ANN models for predicting IG rheological behaviour and supporting material design.</p> Graphical abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial neural network framework development of rheology of ionogels

  • 期刊论文

  • 发表日期：

    2026年1月13日

查看全文
  • Pruthvi S. Marathe,
  • Sashwat Kumar Singh,
  • Namita Karna,
  • Debashis Kundu

摘要

The study presents a predictive approach for the rheological properties of ionogels (IGs), i.e. storage modulus (G’) and loss modulus (G”), using molecular descriptors and Artificial Neural Network (ANN). A total of 5,670 molecular descriptors, generated via alvaDesc and weighted by mole fraction, serve as input parameters, while experimental G’ and G” values sourced from the literature used as outputs. The collected data are divided into training, validation, and testing sets for ANN model development. Hyperparameter optimization using a grid search with the Levenberg-Marquardt algorithm (LMA) achieved a coefficient of determination (R²) of 0.88 and mean squared error (MSE) of 6.9 × 10− 9 for 50% reduced Normalised dataset (60/20/20 split, 5 hidden layers, 0.005 learning rate, 32 batch size, 100 epochs). Scaled Conjugate Gradient (SCG) algorithm is further used to improve emission prediction of ANN model to R2 of 0.96 and MSE of 2.5 × 10− 9 using a network architecture with 3 hidden layers containing 25 neurons each, trained over 200 epochs. In classification tasks, the Terpyridine-3-butylimidazolium salt (Tpy) gelator exhibited the best performance for G’ and G” (R² = 0.98 and 0.95). For hydrophobic ionic liquids (ILs), the perfluorobutanesulfonate (C4F9SO3) group achieved R² of 0.98 for both moduli, while for hydrophilic ILs, (bis(trifluoromethylsulfonyl)amide (TFSA) systems showed R² of 0.96 (G’) and 0.98 (G”). These findings demonstrate the potential of descriptor-based ANN models for predicting IG rheological behaviour and supporting material design.

Graphical abstract