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Toward navigating chemical space of ionic liquids / Natalia Kireeva in Industrial & engineering chemistry research, Vol. 51 N° 44 (Novembre 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 44 (Novembre 2012) . - pp. 14337–14343 Titre : Toward navigating chemical space of ionic liquids : Prediction of melting points using generative topographic maps Type de document : texte imprimé Auteurs : Natalia Kireeva, Auteur ; Sergey L. Kuznetsov, Auteur ; Aslan Yu. Tsivadze, Auteur Année de publication : 2013 Article en page(s) : pp. 14337–14343 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Physical  properties  Ionic  liquids Résumé : In this work, we apply generative topographic maps as a universal approach for data visualization and structure–property modeling of melting points (mp), which is one of the most important physical properties for the design and application of ionic liquids (ILs) as green solvents. Data visualization is part of a more general concept of chemography, which is a relatively new field dealing with visualization of chemical data, representation of chemical space, and navigation in this space. This field has received much attention by chemists as it may help to analyze and to intuitively comprehend relevant molecular features and relationships. In this study, to our knowledge for the first time, we proposed the universal approach that can be used both for the visualization of the chemical space of ILs according to their melting point values and for the development of the classification models able to predict the melting points of novel ILs. The structurally diverse data set of 717 ILs containing bromides of nitrogen-containing organic cations and including 126 pyridinium bromides (PYR), 384 imidazolium and benzoimidazolium bromides (IMZ), and 207 quaternary ammonium bromides (QUAT) was involved in model development. This study was carried out in several descriptor spaces analyzing the impact of descriptor choice. The clear criteria for data visualization and classification quality were used to assess the performance of the developed models. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie3021895 [article] Toward navigating chemical space of ionic liquids : Prediction of melting points using generative topographic maps [texte imprimé] / Natalia Kireeva, Auteur ; Sergey L. Kuznetsov, Auteur ; Aslan Yu. Tsivadze, Auteur . - 2013 . - pp. 14337–14343. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 44 (Novembre 2012) . - pp. 14337–14343 Mots-clés : Physical  properties  Ionic  liquids Résumé : In this work, we apply generative topographic maps as a universal approach for data visualization and structure–property modeling of melting points (mp), which is one of the most important physical properties for the design and application of ionic liquids (ILs) as green solvents. Data visualization is part of a more general concept of chemography, which is a relatively new field dealing with visualization of chemical data, representation of chemical space, and navigation in this space. This field has received much attention by chemists as it may help to analyze and to intuitively comprehend relevant molecular features and relationships. In this study, to our knowledge for the first time, we proposed the universal approach that can be used both for the visualization of the chemical space of ILs according to their melting point values and for the development of the classification models able to predict the melting points of novel ILs. The structurally diverse data set of 717 ILs containing bromides of nitrogen-containing organic cations and including 126 pyridinium bromides (PYR), 384 imidazolium and benzoimidazolium bromides (IMZ), and 207 quaternary ammonium bromides (QUAT) was involved in model development. This study was carried out in several descriptor spaces analyzing the impact of descriptor choice. The clear criteria for data visualization and classification quality were used to assess the performance of the developed models. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie3021895