Abstract：
The determination of the critical micelle concentration (CMC) is a crucial factor when evaluating surfactants, making it an essential tool in studying the properties of surfactants in various industrial fields. In this present research, we assembled a comprehensive set of 593 different classes of surfactants including, anionic, cationic, nonionic, zwitterionic, and Gemini surfactants to establish a link between their molecular structure and the negative logarithmic value of critical micelle concentration (pCMC) utilizing quantitative structure-property relationship (QSPR) methodologies. Statistical analysis revealed that a set of 14 significant Mordred descriptors (SlogP, GATS6d, nAcid, GATS8dv, GATS4dv, PEOE_VSA11, GATS8d, ATS0p, GATS1d, MATS5p, GATS3d, NdssC, GATS6dv and EState_VSA4), along with temperature, served as appropriate inputs. Different machine learning methods, such as multiple linear regression (MLR), random forest regression (RFR), artificial neural network (ANN), and support vector regression (SVM), were employed in this study to build QSPR models. According to the statistical coefficients of QSPR models, SVR with Dragonfly hyperparameter optimization (SVR-DA) was the most accurate in predicting pCMC values, achieving (R2 = 0.9740, Q2 = 0.9739, r‾m2 = 0.9627, and Δrm2 = 0.0244) for the entire dataset.
摘要：
临界胶束浓度(CMC)的确定是评价表面活性剂的一个重要因素,使其成为研究各种工业领域表面活性剂性能的重要工具。在本研究中,我们组装了一套完整的593种不同类别的表面活性剂，包括，阴离子，阳离子,非离子,两性离子，和Gemini表面活性剂利用定量结构-性质关系（QSPR）方法建立其分子结构与临界胶束浓度（pCMC）的负对数值之间的联系。统计分析显示，一组14个显著的Mordred描述符(SlogP，GATS6d,nAcid,GATS8dv,GATS4dv,PEOE_VSA11,GATS8d,ATS0p,GATS1d,MATS5p,GATS3d,NdssC,GATS6dv和EState_VSA4)，随着温度,作为适当的投入。不同的机器学习方法，如多元线性回归(MLR)，随机森林回归(RFR)，人工神经网络(ANN)，和支持向量回归机(SVM)，本研究采用QSPR模型。根据QSPR模型的统计系数，使用Dragonfly超参数优化（SVR-DA）的SVR在预测pCMC值方面最准确，对于整个数据集，实现(R2=0.9740，Q2=0.9739，rm2=0.9627，和Δrm2=0.0244)。
