The industrial application of ionic liquid-catalyzed CO₂ cycloaddition reactions is impeded by harsh conditions. We propose a novel approach that utilizes machine learning and density functional theory (DFT) to overcome this challenge. By training regression algorithms on a data set of 10,174 experimental data points, we developed a predictive model for CO₂ solubility in ionic liquids. The random forest (RF) model exhibited exceptional accuracy, enabling the prediction of the CO₂ solubility in 1624 newly generated ionic liquids. Subsequent experimental validation confirmed the efficacy of the RF model. Moreover, employing the RF model and DFT calculation, we identified four ionic liquids with high CO₂ solubility and low energy barriers for catalytic reactions, presenting promising candidates for efficient CO₂ cycloaddition with epichlorohydrin under mild conditions. This study showcases a streamlined approach to catalyst discovery by integrating machine learning and DFT methods, offering a pathway toward sustainable CO₂ utilization.

中文翻译：

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在温和条件下筛选离子液体以进行高效的 CO2 环加成催化：机器学习和 DFT 相结合的方法


离子液体催化的 CO₂ 环加成反应的工业应用受到恶劣条件的阻碍。我们提出了一种利用机器学习和密度泛函理论 （DFT） 来克服这一挑战的新方法。通过在包含 10,174 个实验数据点的数据集上训练回归算法，我们开发了一个 CO₂ 在离子液体中溶解度的预测模型。随机森林 （RF） 模型表现出极高的准确性，能够预测 CO₂ 在 1624 种新生成的离子液体中的溶解度。随后的实验验证证实了 RF 模型的有效性。此外，采用 RF 模型和 DFT 计算，我们确定了四种具有高 CO₂ 溶解度和低催化反应能垒的离子液体，为在温和条件下使用环氧氯丙烷进行高效 CO₂ 环加成反应提供了有前途的候选者。本研究通过整合机器学习和 DFT 方法展示了一种简化的催化剂发现方法，为可持续利用 CO₂ 提供了一条途径。