Physics-Informed Multifidelity Gaussian Process: Modeling the Effect of Water and Temperature on the Viscosity of a Deep Eutectic Solvent

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.