PNNL

Abstract

The high tunability of deep eutectic solvents (DESs) stems from the ease of changing their precursors and relative compositions. However, measuring physicochemical properties across large composition and temperature ranges, necessary to properly design target-specific DESs, is tedious, error-prone, and represents a bottleneck in the advancement and scalability of DES-based applications. As such, active learning (AL) methodologies based on Gaussian Processes (GPs) were developed in this work to minimize the experimental effort necessary to characterize DESs. Owing to its importance for large scale applications, the reduction of DES viscosity through the addition of a low molecular weight solvent was explored as a case study. A high throughput experimental screening was initially performed on nine different ternary DESs. Then, GPs were successfully trained to predict DES viscosity from its composition and temperature, showcasing the ability of these stochastic, non-parametric models to accurately describe physicochemical properties of complex mixtures. Finally, the ability of GPs to provide estimates of their own uncertainty was leveraged through an AL framework to minimize the amount of data points necessary to obtain accurate viscosity modes. This led to a data amount reduction of as much as 80%, with many systems requiring only five independent viscosity data points to be properly described.