共晶体系
计算机科学
生化工程
纳米技术
计算模拟
工艺工程
管理科学
人工智能
材料科学
计算科学
工程类
复合材料
合金
作者
Dmitry Tolmachev,N. V. Lukasheva,Ruslan R. Ramazanov,Victor M. Nazarychev,Natalia I. Borzdun,Igor V. Volgin,Maria V. Andreeva,Artyom D. Glova,Sofia D. Melnikova,Alexey Yu. Dobrovskiy,Steven A. Silber,Sergey V. Larin,Rafael Maglia de Souza,Mauro C. C. Ribeiro,Sergey V. Lyulin,Mikko Karttunen
摘要
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.
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