Critical micelle concentration (CMC) is one of the main physico-chemical properties of surface-active agents, also known as surfactants, with diverse theoretical and industrial applications. It is influenced by basic parameters such as temperature, pH, salinity, and the chemical structure of surfactants. Most studies have only estimated CMC at fixed conditions based on the surfactant’s chemical parameters. In the present study, we aimed to develop a set of novel and applicable models for estimating CMC of well-known anionic surfactants by considering both the molecular properties of surfactants and basic affecting factors such as salinity, pH, and temperature as modeling parameters. We employed the quantitative-structural property relationship technique to employ the molecular parameters of surfactant ions. We collected 488 CMC values from literature for 111 sodium-based anionic surfactants, including sulfate types, sulfonate, benzene sulfonate, sulfosuccinate, and polyoxyethylene sulfate. We computed 1410 optimized molecular descriptors for each surfactant using Dragon software to be utilized in the modelling processes. The enhanced replacement method was used for selecting the most effective descriptors for the CMC. A multivariate linear model and two non-linear models are the outputs of the present study. The non-linear models were produced using two robust machine learning approaches, stochastic gradient boosting (SGB) trees and genetic programming (GP). Statistical assessment showed highly applicable and acceptable accuracy of the newly developed models (R_SGB² = 0.999395 and R_GP² = 0.954946). The ultimate results showed the superiority and greater ability of the SGB method for making confident predictions.

临界胶束浓度（CMC）是表面活性剂（也称为表面活性剂）的主要物理化学性质之一，具有多种理论和工业应用。它受到温度、pH、盐度和表面活性剂化学结构等基本参数的影响。大多数研究仅根据表面活性剂的化学参数估算固定条件下的 CMC。在本研究中，我们的目标是通过考虑表面活性剂的分子特性和盐度、pH 和温度等基本影响因素作为建模参数，开发一套新颖且适用的模型来估算知名阴离子表面活性剂的 CMC。我们采用定量-结构性质关系技术来利用表面活性剂离子的分子参数。我们从文献中收集了 111 种钠基阴离子表面活性剂的 488 个 CMC 值，包括硫酸盐类型、磺酸盐、苯磺酸盐、磺基琥珀酸盐和聚氧乙烯硫酸盐。我们使用 Dragon 软件计算了每种表面活性剂的 1410 个优化分子描述符，以便在建模过程中使用。使用增强替换方法来选择最有效的 CMC 描述符。本研究的输出是一个多元线性模型和两个非线性模型。非线性模型是使用两种强大的机器学习方法生成的，即随机梯度增强（SGB）树和遗传编程（GP）。统计评估表明新开发的模型具有高度适用性和可接受的准确性（R _SGB ²  = 0.999395 和 R _GP ²  = 0.954946）。最终结果显示了 SGB 方法在做出置信预测方面的优越性和更强的能力。