Molecular dynamics-to-machine learning for deep eutectics in energy storages

In the rapidly evolving landscape of energy storage technologies, the quest for sustainable and efficient solutions is paramount. This review delves into the pivotal role of deep eutectics (DEs) within this domain, exploring their potential through the lens of molecular dynamics (MDs) to machine learning (ML) techniques. By offering a comprehensive synthesis of current research, this work sheds light on the intricate mechanisms and superior physicochemical properties of DEs that make them promising candidates for enhancing energy storage (ESs). It further elucidates the theoretical underpinnings of DEs, including their formation, characteristic features, and the advantages they offer over traditional electrolytes in terms of conductivity, stability, and environmental footprint. Central to the review is the examination of how MDs, supported by ML algorithms, serves as a powerful tool in unraveling the complex interactions and dynamics at the nano-scale. This approach not only accelerates the discovery of optimal DE compositions but also provides predictive insights into their behavior in various electrochemical environments. Moreover, the research critically evaluates the integration of DEs in different types of ESs, including batteries and supercapacitors, highlighting significant advancements and pinpointing areas where further research is required. The discussion extends to the challenges faced in scaling up these technologies for commercial applications, emphasizing the need for multidisciplinary collaboration to overcome these hurdles.