A new machine learning tool for fast ...
Document type :
Article dans une revue scientifique: Article original
DOI :
PMID :
Permalink :
Title :
A new machine learning tool for fast estimation of liquid viscosity. Application to cosmetic oils
Author(s) :
Goussard, Valentin [Auteur]
Unité de Catalyse et Chimie du Solide - UMR 8181 [UCCS]
Duprat, Francois [Auteur]
Chimie Moléculaire, Macromoléculaire et Matériaux (UMR7167) [C3M]
Ploix, Jean-Luc [Auteur]
Chimie Moléculaire, Macromoléculaire et Matériaux (UMR7167) [C3M]
Dreyfus, Gerard [Auteur]
Chimie Moléculaire, Macromoléculaire et Matériaux (UMR7167) [C3M]
Nardello-Rataj, Véronique [Auteur]
Unité de Catalyse et Chimie du Solide (UCCS) - UMR 8181
Aubry, Jean-Marie [Auteur]
Unité de Catalyse et Chimie du Solide (UCCS) - UMR 8181
Unité de Catalyse et Chimie du Solide - UMR 8181 [UCCS]
Duprat, Francois [Auteur]
Chimie Moléculaire, Macromoléculaire et Matériaux (UMR7167) [C3M]
Ploix, Jean-Luc [Auteur]
Chimie Moléculaire, Macromoléculaire et Matériaux (UMR7167) [C3M]
Dreyfus, Gerard [Auteur]
Chimie Moléculaire, Macromoléculaire et Matériaux (UMR7167) [C3M]
Nardello-Rataj, Véronique [Auteur]

Unité de Catalyse et Chimie du Solide (UCCS) - UMR 8181
Aubry, Jean-Marie [Auteur]

Unité de Catalyse et Chimie du Solide (UCCS) - UMR 8181
Journal title :
Journal of Chemical Information and Modeling
Abbreviated title :
J Chem Inf Model
Publication date :
2020-04-27
ISSN :
1549-960X
HAL domain(s) :
Chimie/Chimie organique
English abstract : [en]
The viscosities of pure liquids are estimated at 25 °C, from their molecular structures, using three modeling approaches: group contributions, COSMO-RS σ-moment-based neural networks, and graph machines. The last two are ...
Show more >The viscosities of pure liquids are estimated at 25 °C, from their molecular structures, using three modeling approaches: group contributions, COSMO-RS σ-moment-based neural networks, and graph machines. The last two are machine-learning methods, whereby models are designed and trained from a database of viscosities of 300 molecules at 25 °C. Group contributions and graph machines make use of the 2D-structures only (the SMILES codes of the molecules), while neural networks estimations are based on a set of five descriptors: COSMO-RS σ-moments. For the first time, leave-one-out is used for graph machine selection, and it is shown that it can be replaced with the much faster virtual leave-one-out algorithm. The database covers a wide diversity of chemical structures, namely, alkanes, ethers, esters, ketones, carbonates, acids, alcohols, silanes, and siloxanes, as well as different chemical backbone, i.e., straight, branched, or cyclic chains. A comparison of the viscosities of liquids of an independent set of 22 cosmetic oils shows that the graph machine approach provides the most accurate results given the available data. The results obtained by the neural network based on sigma-moments and by the graph machines can be duplicated easily by using a demonstration tool based on the Docker technology, available for download as explained in the Supporting Information. This demonstration also allows the reader to predict, at 25 °C, the viscosity of any liquid of moderate molecular size (MShow less >
Show more >The viscosities of pure liquids are estimated at 25 °C, from their molecular structures, using three modeling approaches: group contributions, COSMO-RS σ-moment-based neural networks, and graph machines. The last two are machine-learning methods, whereby models are designed and trained from a database of viscosities of 300 molecules at 25 °C. Group contributions and graph machines make use of the 2D-structures only (the SMILES codes of the molecules), while neural networks estimations are based on a set of five descriptors: COSMO-RS σ-moments. For the first time, leave-one-out is used for graph machine selection, and it is shown that it can be replaced with the much faster virtual leave-one-out algorithm. The database covers a wide diversity of chemical structures, namely, alkanes, ethers, esters, ketones, carbonates, acids, alcohols, silanes, and siloxanes, as well as different chemical backbone, i.e., straight, branched, or cyclic chains. A comparison of the viscosities of liquids of an independent set of 22 cosmetic oils shows that the graph machine approach provides the most accurate results given the available data. The results obtained by the neural network based on sigma-moments and by the graph machines can be duplicated easily by using a demonstration tool based on the Docker technology, available for download as explained in the Supporting Information. This demonstration also allows the reader to predict, at 25 °C, the viscosity of any liquid of moderate molecular size (MShow less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
CNRS
Centrale Lille
ENSCL
Univ. Artois
Université de Lille
Centrale Lille
ENSCL
Univ. Artois
Université de Lille
Collections :
Research team(s) :
Colloïdes catalyse oxydation (CÏSCO)
Submission date :
2022-03-02T07:13:09Z
2023-12-15T20:35:27Z
2023-12-15T20:35:27Z