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QSPR Modeling of Liquid-liquid Equilibria in Two-phase Systems of Water and Ionic Liquid.
Molecular Informatics ( IF 3.6 ) Pub Date : 2020-05-29 , DOI: 10.1002/minf.202000001
Kyrylo Oleksandrovych Klimenko 1 , João Miguel Inês 1 , José Manuel Silva Simões Esperança 1 , Luís Paulo Nieto Rebelo 1 , João Aires-de-Sousa 1 , Gonçalo Valente Silva Mariño Carrera 1
Affiliation  

The increasing application of new ionic liquids (IL) creates the need of liquid‐liquid equilibria data for both miscible and quasi‐immiscible systems. In this study, equilibrium concentrations at different temperatures for ionic liquid+water two‐phase systems were modeled using a Quantitative‐Structure‐Property Relationship (QSPR) method. Data on equilibrium concentrations were taken from the ILThermo Ionic Liquids database, curated and used to make models that predict the weight fraction of water in ionic liquid rich phase and ionic liquid in the aqueous phase as two separate properties. The major modeling challenge stems from the fact that each single IL is characterized by several data points, since equilibrium concentrations are temperature dependent. Thus, new approaches for the detection of potential data point outliers, testing set selection, and quality prediction have been developed. Training set comprised equilibrium concentration data for 67 and 68 ILs in case of water in IL and IL in water modeling, respectively. SiRMS, MOLMAPS, Rcdk and Chemaxon descriptors were used to build Random Forest models for both properties. Models were subjected to the Y‐scrambling test for robustness assessment. The best models have also been validated using an external test set that is not part of the ILThermo database. A two‐phase equilibrium diagram for one of the external test set IL is presented for better visualization of the results and potential derivation of tie lines.

中文翻译:

水和离子液体两相系统中液-液平衡的 QSPR 建模。

新型离子液体 (IL) 的应用越来越多,因此需要混溶和准不混溶系统的液-液平衡数据。在这项研究中,离子液体+水两相系统在不同温度下的平衡浓度使用定量-结构-性能关系(QSPR)方法进行建模。平衡浓度数据取自 ILThermo 离子液体数据库,经过整理并用于制作模型,预测富离子液体相中的水和水相中的离子液体的重量分数作为两个独立的属性。主要的建模挑战源于这样一个事实,即每个单个 IL 都有几个数据点,因为平衡浓度与温度有关。因此,检测潜在数据点异常值的新方法,已经开发了测试集选择和质量预测。训练集分别包含 67 和 68 IL 的平衡浓度数据,分别是在 IL 中的水和水建模中的 IL 的情况下。SirMS、MOLMAPS、Rcdk 和 Chemaxon 描述符用于为这两个属性构建随机森林模型。模型经过 Y 加扰测试以进行稳健性评估。还使用不属于 ILThermo 数据库的外部测试集验证了最佳模型。提供了一个外部测试集 IL 的两相平衡图,以更好地可视化结果和潜在的联系线推导。Rcdk 和 Chemaxon 描述符用于为这两个属性构建随机森林模型。模型经过 Y 加扰测试以进行稳健性评估。还使用不属于 ILThermo 数据库的外部测试集验证了最佳模型。提供了一个外部测试集 IL 的两相平衡图,以更好地可视化结果和潜在的联系线推导。Rcdk 和 Chemaxon 描述符用于为这两个属性构建随机森林模型。模型经过 Y 加扰测试以进行稳健性评估。还使用不属于 ILThermo 数据库的外部测试集验证了最佳模型。提供了一个外部测试集 IL 的两相平衡图,以更好地可视化结果和潜在的联系线推导。
更新日期:2020-05-29
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