Simulation of deep eutectic solvents: Progress to promises

Deep eutectic solvents (DESs) are binary or ternary mixtures of compounds that possess significant melting point depressions relative to the pure isolated components. The discovery of DESs has been a major breakthrough with multiple fields benefitting from their low cost and tunable physiochemical properties. However, tailoring DESs for specific applications through their practically unlimited synthetic combinations can be as much a hindrance as a benefit given the expense and time-required to perform large-scale experimental measurements. This emphasizes the need for fast computational tools capable of making accurate predictions of DES physiochemical properties exclusively from molecular structure. Yet, these systems are not trivial to model or simulate at the atomic level given their exceedingly nonideal behaviors, asymmetry of components, and the complexity of their molecular electrostatic interactions. Despite the challenge, computational reports featuring quantum mechanical (QM) methods have provided significant understanding into the relationship between the melting point depression and the unique and complex hydrogen bond network present in DESs. Classical molecular dynamics (MD) methods have examined bulk-phase solvent organization in conjunction with thermodynamic and transport properties. Machine learning (ML) algorithms have shown great potential as structure-property prediction tools. Overall, this review highlights computational accomplishments that have meaningfully advanced our understanding of DESs and strives to give the reader a sense of the overall strengths and drawbacks of the methodologies employed while hinting at promises of advances to come. This article is categorized under: Software > Simulation Methods

Automated summary

Deep eutectic solvents (DESs) are mixtures of compounds with significant melting point depressions. The discovery of DESs has been a major breakthrough, benefiting various fields with their low cost and adjustable physiochemical properties. However, customizing DESs for specific applications can be challenging due to the expense and time required. Fast computational tools capable of predicting DES properties from molecular structure are needed. Quantum mechanical (QM) methods, classical molecular dynamics (MD) methods, and machine learning (ML) algorithms have contributed to the understanding and prediction of DES properties. This review highlights the computational achievements and discusses the strengths and drawbacks of the methodologies used.