Huwaimel Bader, Alanazi Jowaher, Alanazi Muteb, Alharby Tareq Nafea, Alshammari Farhan
Department of Pharmaceutical Chemistry, College of Pharmacy, University of Ha'il, Hail, 81442, Saudi Arabia.
Medical and Diagnostic Research Center, University of Ha'il, Hail, 55473, Saudi Arabia.
Sci Rep. 2024 Dec 30;14(1):31857. doi: 10.1038/s41598-024-82989-1.
This research article presents a thorough and all-encompassing examination of predictive models utilized in the estimation of viscosity for ionic liquid solutions. The study focuses on crucial input parameters, namely the type of cation, the type of anion, the temperature (measured in Kelvin), and the concentration of the ionic liquid (expressed in mol%). This study assesses three influential machine learning algorithms that are based on the Decision Tree methodology. These algorithms include Random Forest (RF), Gradient Boosting (GB), and XGBoost (XGB). Furthermore, the study incorporates the use of Glowworm Swarm Optimization (GSO) for hyper-parameter optimization, thereby further elevating the efficacy of the models. The results obtained from the evaluation showcase the exceptional predictive capabilities of the models, with Random Forest (RF) achieving an impressive R value of 0.9971, Gradient Boosting (GB) attaining an R value of 0.9916, and XGBoost (XGB) yielding an R value of 0.9911. In addition to the R metric, the study also presents other performance metrics, such as RMSE and MAPE, for each model. This comprehensive assessment of accuracy further solidifies the credibility and effectiveness of the models employed in the estimation of viscosity for ionic liquid solutions.
这篇研究文章对用于估计离子液体溶液粘度的预测模型进行了全面且详尽的考察。该研究聚焦于关键输入参数，即阳离子类型、阴离子类型、温度（以开尔文为单位测量）以及离子液体的浓度（以摩尔百分比表示）。本研究评估了三种基于决策树方法的有影响力的机器学习算法。这些算法包括随机森林（RF）、梯度提升（GB）和极端梯度提升（XGB）。此外，该研究采用萤火虫群优化（GSO）进行超参数优化，从而进一步提高模型的效能。评估所得结果展示了模型卓越的预测能力，随机森林（RF）的R值达到了令人瞩目的0.9971，梯度提升（GB）的R值为0.9916，极端梯度提升（XGB）的R值为0.9911。除了R指标外，该研究还给出了每个模型的其他性能指标，如均方根误差（RMSE）和平均绝对百分比误差（MAPE）。这种对准确性的全面评估进一步巩固了用于估计离子液体溶液粘度的模型的可信度和有效性。
