Properties of ionic liquids (ILs) are required for the design of products and processes involving ILs. Although innumerable ILs may be generated through the combination of a variety of cations, anions, and substituents, only a small part of them have been reported to exist (have been synthesized). The available experimental data are generally limited and sometimes even contradictory. A detailed knowledge about the properties of ILs is critically important, especially for ILs not yet available. Based on collected experimental data from numerous literature sources, a series of group contribution models have been developed for estimating various properties (density, heat capacity, viscosity, surface tension, melting point) of ILs. To evaluate the predictive capability of the proposed or employed group contribution models, nearly 70% of the data sets (i.e., training sets) are used for correlation, and then the remaining data sets (i.e., test sets) not included in the training sets are used for prediction. The calculation results show that the proposed group contribution models can predict the properties of studied ILs with sufficient accuracy. These property estimation models can both be used easily and also provide estimation of important properties for previously unstudied ILs, some of which may be considered as potential solvents in many industrial applications.

中文翻译：

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基于基团贡献的离子液体性质估算方法

离子液体（ILs）的特性对于涉及ILs的产品和工艺设计是必需的。尽管可能通过各种阳离子，阴离子和取代基的组合生成无数个IL，但据报道仅存在其中的一小部分（已合成）。现有的实验数据通常是有限的，有时甚至是矛盾的。关于IL的特性的详细知识至关重要，尤其是对于尚不可用的IL。基于从众多文献来源收集到的实验数据，已开发了一系列的组贡献模型，用于估算IL的各种特性（密度，热容量，粘度，表面张力，熔点）。为了评估提议或采用的小组贡献模型的预测能力，将近70％的数据集（即训练集）用于关联，然后将未包括在训练集中的其余数据集（即测试集）用于预测。计算结果表明，所提出的群体贡献模型可以足够准确地预测所研究的IL的性质。这些属性估计模型既可以轻松使用，也可以为以前未研究的IL提供重要属性的估计，其中一些在许多工业应用中可能被视为潜在的溶剂。