Predictive and Generative AI Approaches to the Design of Deep Eutectic Solvents

Abstract Deep eutectic solvents (DESs) have emerged as promising sustainable alternatives to conventional solvents due to their potential low toxicity, biodegradability, and low flammability. While their complex nature makes experimental characterization costly and time consuming, the advent of artificial intelligence (AI) has recently paved the way to a revolutionary paradigm for chemical discovery, offering powerful tools not only for predicting properties of DES mixtures but also for generating de novo DESs with optimized properties. After a concise overview of foundational concepts in AI, this Short Review explores the application of both predictive and generative machine learning (ML) models to the design and characterization of DESs.