Ionic liquids (ILs) are regarded as unique, attractive kinds of solvents, which can be utilized in carbon dioxide (CO₂) capture processes. The preparation and design of such processes need simple and accurate models to predict solubility properties. This study incorporates the potential of the molecular descriptors, structural information, and its direct effect on the estimation of CO₂ solubility in pyridinium-based IL mixtures. Using a collection of 430 experimental measurements, two different feed-forward back-propagation neural network models with three and five input variables were developed. In the first scenario, molecular weight, absorption temperature, and equilibrium pressure were considered as the model input. In contrast, the second scenario presents a new descriptor-based chemoinformatics model with the input data of the structural information for the estimation of the CO₂ solubility in pyridinium-based ILs. To depict the structural clue of the various solvents, the presence of ether groups as a categoric factor, and the number of carbon in hydrocarbon chain and, or ether groups in functionalized pyridinium-based ILs were identified as the two input parameters in our novel descriptive model. The network architecture, including the neurons’ number, training, and transfer functions, is optimized. The statistical analysis of the obtained results illustrated that the developed molecular descriptor-based model, with the estimated Root Mean Square Error (RMSE) and R² of 9.06-E03 and 0.995 for the test data, can be employed for the detailed assessment of the CO₂ loading capacities of pyridinium-based IL solutions. The authors believe that the out-perform of the ready-made descriptor-based model can be helpful as a guided example for all kinds of descriptor-based modeling campaigns.

离子液体 (IL) 被认为是一种独特的、有吸引力的溶剂，可用于二氧化碳 (CO ₂ ) 捕获过程。此类工艺的制备和设计需要简单而准确的模型来预测溶解度特性。本研究结合了分子描述符的潜力、结构信息及其对 CO ₂估计的直接影响在基于吡啶的离子液体混合物中的溶解度。使用 430 个实验测量的集合，开发了两个不同的具有三个和五个输入变量的前馈反向传播神经网络模型。在第一种情况下，分子量、吸收温度和平衡压力被视为模型输入。与此相反，第二场景呈现的结构信息对CO的估计输入数据的新的基于描述符的化学信息学模型₂在基于吡啶的离子液体中的溶解度。为了描述各种溶剂的结构线索，醚基团作为分类因子的存在，以及烃链中的碳数和/或官能化吡啶基离子液体中的醚基团被确定为我们新描述中的两个输入参数模型。优化了网络架构，包括神经元的数量、训练和传递函数。对所得结果的统计分析表明，所开发的基于分子描述符的模型，估计均方根误差 ( RMSE ) 和R ²为 9.06-E03，测试数据为 0.995，可用于详细评估CO ₂基于吡啶的离子液体溶液的负载能力。作者认为，现成的基于描述符的模型的表现可能有助于作为各种基于描述符的建模活动的指导示例。