| Literature DB >> 35054840 |
Dmitry Tolmachev1, Natalia Lukasheva1, Ruslan Ramazanov1, Victor Nazarychev1, Natalia Borzdun1, Igor Volgin1, Maria Andreeva1, Artyom Glova1, Sofia Melnikova1, Alexey Dobrovskiy1, Steven A Silber2,3, Sergey Larin1, Rafael Maglia de Souza4, Mauro Carlos Costa Ribeiro4, Sergey Lyulin1, Mikko Karttunen1,2,3,5.
Abstract
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.Entities:
Keywords: computer simulation; deep eutectic solvents; machine leaning; molecular dynamics; quantum mechanics
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Year: 2022 PMID: 35054840 PMCID: PMC8775846 DOI: 10.3390/ijms23020645
Source DB: PubMed Journal: Int J Mol Sci ISSN: 1422-0067 Impact factor: 5.923