Ionic liquids (ILs) and deep eutectic solvents (DESs) are regarded as the next generation solvents for carbon capture which consist of cations and anions. Thousands of combinations of cations and anions can lead to varied properties of ILs/DESs, which makes it difficult to screen such ILs/DESs for CO₂ in experiments. Computer-aided molecular design (CAMD) saves time and cost by reversing the search for the structure of ILs that are suitable for carbon capture. Compared with other thermodynamic models, machine learning (ML) models have the advantages of efficiency and accuracy in CAMD; hence, the number of studies on the application of ML models in the field of CAMD is growing each year. In this paper, a concise review of the application of ML to ILs/DESs-based CO₂ capture technology is provided. The development process of ML models in (1) the prediction of the properties of ILs/DESs using their structure; and (2) the prediction of the carbon capture effect using process parameters is discussed. Perspectives on future research directions are proposed and key challenges are identified for screening suitable ILs/DESs using the capture effectiveness of a specific carbon capture process as an evaluation criterion.

离子液体(ILs) 和低共熔溶剂 (DESs) 被认为是用于碳捕获的下一代溶剂，由阳离子和阴离子组成。数以千计的阳离子和阴离子组合可导致 ILs/DESs 的不同性质，这使得在实验中很难筛选此类 ILs/DESs 的 CO _{2 。}计算机辅助分子设计 (CAMD) 通过逆向寻找适用于碳捕获的离子液体结构来节省时间和成本。与其他热力学模型相比，机器学习 (ML) 模型在 CAMD 中具有效率和准确性的优势；因此，关于 ML 模型在 CAMD 领域应用的研究数量每年都在增长。_{在本文中，简要回顾了 ML 在基于 ILs/DESs 的 CO 2}中的应用提供捕获技术。ML 模型的开发过程在 (1) 利用其结构预测 ILs/DESs 的特性；(2) 讨论了使用工艺参数预测碳捕获效果。提出了未来研究方向的观点，并确定了使用特定碳捕获过程的捕获效率作为评估标准筛选合适的 ILs/DES 的关键挑战。