Nowadays, greenhouse gas CO₂ emissions have caused serious global warming problems. Unique properties of ionic liquids (ILs), such as negligible vapor pressure, good thermal and chemical stability, high gas dissolution capacity, etc., have made them highly promising in capturing CO₂. Although researchers have done a lot of experimental work using ILs to capture CO₂, time-consuming and high experimental economic costs have led to a strong interest in establishing predictive models. In this work, 297 experimental data points including 16 cations and 9 anions for 34 ILs are collected and the structures of cations and anions are optimized by quantum chemistry. Then the electrostatic potential surface area (S_EP) and charge distribution area (S_σ-profile) descriptors are calculated and used to predict the Henry's law constant (HLC) of CO₂ in ILs. Three new models, namely, the multiple linear regression (MLR), support vector machine (SVM), and extreme learning machine (ELM) are finally developed based on the above-calculated descriptors. Results show that the ELM model with AARD = 3.22% for the entire data set is the most valid and powerful one to predict the HLC of CO₂.

如今，温室气体CO _2的排放已引起严重的全球变暖问题。离子液体（ILs）的独特特性，例如可忽略的蒸气压，良好的热和化学稳定性，高的气体溶解能力等，使其在捕获CO _2方面具有很高的前景。尽管研究人员已经完成了大量使用IL捕获CO ₂的实验工作，但耗时且高昂的实验经济成本已引起人们对建立预测模型的浓厚兴趣。在这项工作中，收集了297个实验数据点，包括针对34个IL的16个阳离子和9个阴离子，并通过量子化学对阳离子和阴离子的结构进行了优化。然后，静电势表面积（小号_EP）和电荷分布区域（小号_Σ-轮廓）描述符被计算并用于预测CO的亨利定律常数（HLC）₂在离子液体。基于以上计算的描述符，最终开发了三种新模型，即多元线性回归（MLR），支持向量机（SVM）和极限学习机（ELM）。结果表明，整个数据集的AARD = 3.22％的ELM模型是预测CO ₂的HLC的最有效和最有力的模型。