The development of models that accurately predict the formation of eutectic mixtures (EMs, including the well-known deep eutectic solvents) and their viscosity is imperative to save time in synthesizing new solvents. We developed reliable machine-learning-based classifiers able to discern between eutectic and noneutectic (non-EM) mixtures and regressors able to predict the viscosity of an EM. A new experimental data set of 219 EMs, 384 non-EMs, and 1450 viscosity points at different temperatures and water contents is provided and used to challenge several models, defined both by an algorithm and by descriptors. The top-performing EM/non-EM classifier yields an accuracy of 92%, and the best regressor achieves viscosity predictions with a mean absolute error of 2.2 mPa·s; the extrapolation capabilities of the latter were assessed on additional measurements at temperatures and water contents outside the range of the training data set, revealing good accuracy at low viscosities. The SHapley Additive exPlanations (SHAP) algorithm was employed in several models as an eXplainable Artificial Intelligence (XAI) technique to quantify input feature contributions to the model output. These results represent a significant step forward in developing robust and highly accurate models for determining eutectic mixtures and their viscosity.

中文翻译：

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基于机器学习的高效新工具，用于设计共晶混合物并预测其粘度


开发准确预测共晶混合物 （EMs，包括众所周知的深共熔溶剂） 的形成及其粘度的模型对于节省合成新溶剂的时间至关重要。我们开发了可靠的基于机器学习的分类器，能够区分共晶和非共晶（非 EM）混合物，以及能够预测 EM 粘度的回归器。提供了一个新的实验数据集，其中包含不同温度和含水量下的 219 个 EM、384 个非 EM 和 1450 个粘度点，并用于挑战由算法和描述符定义的多个模型。性能最佳的 EM/非 EM 分类器的准确率为 92%，最佳回归器实现粘度预测，平均绝对误差为 2.2 mPa·s;在训练数据集范围之外的温度和含水量下的额外测量中评估了后者的外推能力，揭示了在低粘度下的良好准确性。SHapley 加法解释 （SHAP） 算法在多个模型中用作可解释的人工智能 （XAI） 技术，以量化输入特征对模型输出的贡献。这些结果代表了在开发用于确定共晶混合物及其粘度的稳健且高精度的模型方面向前迈出了重要一步。