Deep eutectic solvents (DESs) are novel green solvents. Potential applications of DESs require a knowledge of their physical and thermodynamic properties. This study is devoted to the DES heat capacity. Since the potential number of DESs to be prepared in the future is innumerable, it is vital to have predictive models. In this study, two machine learning models, namely, the multilayer perceptron artificial neural network (MLPANN) and the least square support vector machine (LSSVM) were coupled with the group contribution (GC) and atomic contribution (AC) approaches. In the contribution methods, each structural fragment of the compounds is considered as input to the machine learning models, significantly enhancing predictive capability. A comprehensive database was collected, including 640 data points from 51 different DESs at various temperatures. The MLPANN-GC and LSSVM-GC models resulted in AARD% values of 1.74 and 1.73%, respectively, while the corresponding values were 2.90 and 2.64% for the MLPANN-AC and LSSVM-AC models.

中文翻译：

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使用耦合原子/群贡献和机器学习方法的深共晶溶剂热容的新型预测模型


深共熔溶剂 （DES） 是新型绿色溶剂。DES 的潜在应用需要了解其物理和热力学特性。本研究致力于 DES 热容。由于未来可能需要准备的 DES 数量数不胜数，因此拥有预测模型至关重要。在本研究中，将多层感知器人工神经网络 （MLPANN） 和最小二乘支持向量机 （LSSVM） 两种机器学习模型与组贡献 （GC） 和原子贡献 （AC） 方法相结合。在贡献方法中，化合物的每个结构片段都被视为机器学习模型的输入，从而显着增强了预测能力。收集了一个全面的数据库，包括来自不同温度下 51 个不同 DES 的 640 个数据点。MLPANN-GC 和 LSSVM-GC 模型的 AARD% 值分别为 1.74% 和 1.73%，而 MLPANN-AC 和 LSSVM-AC 模型的相应值为 2.90% 和 2.64%。