Modeling from Theory and Modeling from Data: Complementary or Alternative Approaches? The Case of Ionic Liquids

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• 作者：Dr. Alessio Paternò ; Dr. Laura Goracci ; Prof. Salvatore Scire and Prof. Giuseppe Musumarra
• 刊名：ChemistryOpen
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：6
• 期：1
• 页码：90-101
• 全文大小：1528K
• ISSN：2191-1363

文摘

In the field of ionic liquids (ILs), theory-driven modeling approaches aimed at the best fit for all available data by using a unique, and often nonlinear, model have been widely adopted to develop quantitative structure–property relationship (QSPR) models. In this context, we propose chemoinformatic and chemometric data-driven procedures that lead to QSPR soft models with local validity that are able to predict relevant physicochemical properties of ILs, such as viscosity, density, decomposition temperature, and conductivity. These models, which use readily available and easily interpretable VolSurf+ descriptors, represent an unexploited opportunity for experimentalists to model and predict the physicochemical properties of ILs in industrial R&D design.