Deep eutectic solvents (DESs) are gaining recognition as environmentally friendly solvent alternatives for diverse chemical processes. Yet, designing DESs tailored to specific applications is a resource-intensive task, which requires an accurate estimation of their physicochemical properties. Among them, viscosity is crucial, as it often dictates a DES’s suitability as a solvent. In this study, an artificial neural network (ANN) is introduced to accurately describe the viscosity of DESs and their mixtures with cosolvents. The ANN utilizes molecular parameters derived from σ-profiles, computed using the conductor-like screening model for the real solvent segment activity coefficient (COSMO-SAC). The data set comprises 1891 experimental viscosity measurements for 48 DESs based on choline chloride, encompassing 279 different compositions, along with 1618 data points of DES mixtures with cosolvents as water, methanol, isopropanol, and dimethyl sulfoxide, covering a wide range of viscosity measurements from 0.3862 to 4722 mPa s. The optimal ANN structure for describing the logarithmic viscosity of DESs is configured as 9-19-16-1, achieving an overall average absolute relative deviation of 1.6031%. More importantly, the ANN shows a remarkable extrapolation capacity, as it is capable of predicting the viscosity of systems including solvents (ethanol) and hydrogen bond donors (2,3-butanediol) not considered in the training. The ANN model also demonstrates an extensive applicability domain, covering 94.17% of the entire database. These achievements represent a significant step forward in developing robust, open source, and highly accurate models for DESs using molecular descriptors.

中文翻译：

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评估可持续低共​​熔溶剂和共溶剂混合物的粘度：基于人工神经网络的分子方法


低共熔溶剂 (DES) 作为多种化学工艺的环保溶剂替代品正在获得认可。然而，设计适合特定应用的 DES 是一项资源密集型任务，需要准确估计其物理化学性质。其中，粘度至关重要，因为它通常决定 DES 作为溶剂的适用性。在本研究中，引入人工神经网络（ANN）来准确描述 DES 及其与助溶剂的混合物的粘度。 ANN 利用源自​​ σ 分布的分子参数，并使用真实溶剂段活度系数 (COSMO-SAC) 的类导体筛选模型进行计算。该数据集包含 48 种基于氯化胆碱的 DES 的 1891 个实验粘度测量值，涵盖 279 种不同的成分，以及 DES 混合物与水、甲醇、异丙醇和二甲亚砜等共溶剂的 1618 个数据点，涵盖了广泛的粘度测量值0.3862 至 4722 毫帕·秒。用于描述DES对数粘度的最佳ANN结构配置为9-19-16-1，总体平均绝对相对偏差为1.6031%。更重要的是，人工神经网络显示出显着的外推能力，因为它能够预测训练中未考虑的包括溶剂（乙醇）和氢键供体（2,3-丁二醇）在内的系统的粘度。 ANN 模型还展示了广泛的适用范围，覆盖了整个数据库的 94.17%。这些成就代表着在使用分子描述符开发稳健、开源和高度准确的 DES 模型方面向前迈出了重要一步。