Deep eutectic solvents (DES) are a subclass of ionic liquids formed from a eutectic mixture of Lewis or Bronsted acids and bases, which can contain a variety of anionic and/or cationic species. They are promising electrolyte candidates for a number of application fields, such as supercapacitors and batteries. We will present work on (i) establishing modeling protocols for the computational prediction of key properties of DES systems and their constituent components; (ii) creating large-scale virtual libraries of DES candidates; (iii) characterizing and assessing these candidates using the before-mentioned modeling protocols; (iv) identifying high-value targets with promising property profiles; and (v) using machine learning and other data mining techniques to develop a better understanding of the structure-property relationships that yield design rules for next-generation DES systems.

Modeling, virtual high-throughput screening, and machine learning of deep eutectic solvents

2019 Spring National Meeting

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.