Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia.
Department of Physics and Astronomy, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada.
Int J Mol Sci. 2022 Jan 7;23(2):645. doi: 10.3390/ijms23020645.
Deep eutectic solvents (DESs) are one of the most rapidly evolving types of solvents, appearing in a broad range of applications, such as nanotechnology, electrochemistry, biomass transformation, pharmaceuticals, membrane technology, biocomposite development, modern 3D-printing, and many others. The range of their applicability continues to expand, which demands the development of new DESs with improved properties. To do so requires an understanding of the fundamental relationship between the structure and properties of DESs. Computer simulation and machine learning techniques provide a fruitful approach as they can predict and reveal physical mechanisms and readily be linked to experiments. This review is devoted to the computational research of DESs and describes technical features of DES simulations and the corresponding perspectives on various DES applications. The aim is to demonstrate the current frontiers of computational research of DESs and discuss future perspectives.
深共熔溶剂(DESs)是发展最快的溶剂类型之一,在许多领域都有广泛的应用,如纳米技术、电化学、生物质转化、制药、膜技术、生物复合材料开发、现代 3D 打印等。它们的应用范围还在不断扩大,这就需要开发具有改进性能的新型 DESs。要做到这一点,就需要了解 DESs 的结构和性能之间的基本关系。计算机模拟和机器学习技术提供了一种富有成效的方法,因为它们可以预测和揭示物理机制,并且可以很容易地与实验联系起来。这篇综述致力于 DESs 的计算研究,描述了 DES 模拟的技术特点以及各种 DES 应用的相应视角。目的是展示 DESs 计算研究的当前前沿,并讨论未来的前景。