Fleck Maximilian, Darouich Samir, Pleiss Jürgen, Hansen Niels, Spera Marcelle B M
Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany.
Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany.
J Chem Inf Model. 2025 Apr 28;65(8):3999-4009. doi: 10.1021/acs.jcim.5c00157. Epub 2025 Apr 16.
Knowledge of shear viscosity as function of temperature and composition of an aqueous deep eutectic solvent mixture is essential for process design but can be highly challenging and costly to measure. The present work proposes to combine a small set of experimentally determined viscosities with a small set of simulated values within a linear multifidelity approach to predict the dependency of shear viscosity on temperature and composition. This method provides a simple approach that requires a physics-based transformation of viscosity data prior to training, without the need for additional data such as densities. This allows reduction in cost with experiments and reduces the number of experiments and simulations required to characterize a specific system. The data-driven component of the model does not concern the viscosity itself but rather the excess free energy term within the framework of a mixture viscosity model according to Eyring's absolute rate theory. Moreover, we illustrate the application of kernel-based machine learning approaches to daily research questions where data availability is limited compared to the data set size typically required for neural networks.
了解剪切粘度作为水基低共熔溶剂混合物温度和组成的函数，对于工艺设计至关重要，但测量起来可能极具挑战性且成本高昂。目前的工作建议在一种线性多保真度方法中，将一小部分实验测定的粘度与一小部分模拟值相结合，以预测剪切粘度对温度和组成的依赖性。该方法提供了一种简单的途径，在训练之前需要对粘度数据进行基于物理的转换，而无需诸如密度等额外数据。这使得实验成本得以降低，并减少了表征特定系统所需的实验和模拟次数。模型的数据驱动部分并不涉及粘度本身，而是根据艾林绝对速率理论在混合粘度模型框架内的过量自由能项。此外，我们说明了基于核的机器学习方法在日常研究问题中的应用，在这些问题中，与神经网络通常所需的数据集大小相比，数据可用性有限。
