Computer Simulations of Deep Eutectic Solvents: Challenges, Solutions, and Perspectives

Autor:	Dmitry Tolmachev, Natalia Lukasheva, Ruslan Ramazanov, Victor Nazarychev, Natalia Borzdun, Igor Volgin, Maria Andreeva, Artyom Glova, Sofia Melnikova, Alexey Dobrovskiy, Steven A. Silber, Sergey Larin, Rafael Maglia de Souza, Mauro Carlos Costa Ribeiro, Sergey Lyulin, Mikko Karttunen
Rok vydání:	2022
Předmět:	SIMULAÇÃO Drug Industry QH301-705.5 Review Molecular Dynamics Simulation Catalysis Machine Learning Inorganic Chemistry Electrochemistry computer simulation Nanotechnology Biomass Biology (General) Physical and Theoretical Chemistry QD1-999 Molecular Biology Spectroscopy deep eutectic solvents applied_chemistry quantum mechanics Organic Chemistry General Medicine molecular dynamics Computer Science Applications Chemistry Quantum Theory machine leaning
Zdroj:	International Journal of Molecular Sciences, Vol 23, Iss 645, p 645 (2022) International Journal of Molecular Sciences Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP
ISSN:	1422-0067
DOI:	10.3390/ijms23020645
Popis:	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 provide predictions, 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.
Databáze:	OpenAIRE
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