Fast and Accurate Prediction of Refractive Index of Organic Liquids with Graph Machines.

Duprat, F.; Ploix, J. L.; Aubry, Jean-Marie; Gaudin, T.

Document type :

Article dans une revue scientifique: Article original

DOI :

10.3390/molecules28196805

PMID :

37836648

Permalink :

http://hdl.handle.net/20.500.12210/103481

Title :

Fast and Accurate Prediction of Refractive Index of Organic Liquids with Graph Machines.

Author(s) :

Duprat, F. [Auteur]
Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris [ESPCI Paris]
Ploix, J. L. [Auteur]
Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris [ESPCI Paris]
Aubry, Jean-Marie [Auteur]

Unité de Catalyse et Chimie du Solide (UCCS) - UMR 8181
Gaudin, T. [Auteur]

Journal title :

Molecules

Abbreviated title :

Molecules

Volume number :

Publication date :

2023-10-16

ISSN :

1420-3049

English abstract : [en]

The refractive index (RI) of liquids is a key physical property of molecular compounds and materials. In addition to its ubiquitous role in physics, it is also exploited to impart specific optical properties (transparency, ...
Show more >The refractive index (RI) of liquids is a key physical property of molecular compounds and materials. In addition to its ubiquitous role in physics, it is also exploited to impart specific optical properties (transparency, opacity, and gloss) to materials and various end-use products. Since few methods exist to accurately estimate this property, we have designed a graph machine model (GMM) capable of predicting the RI of liquid organic compounds containing up to 16 different types of atoms and effective in discriminating between stereoisomers. Using 8267 carefully checked RI values from the literature and the corresponding 2D organic structures, the GMM provides a training root mean square relative error of less than 0.5%, i.e., an RMSE of 0.004 for the estimation of the refractive index of the 8267 compounds. The GMM predictive ability is also compared to that obtained by several fragment-based approaches. Finally, a Docker-based tool is proposed to predict the RI of organic compounds solely from their SMILES code. The GMM developed is easy to apply, as shown by the video tutorials provided on YouTube.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
CNRS
Centrale Lille
ENSCL
Univ. Artois

Collections :

Unité de Catalyse et Chimie du Solide (UCCS) - UMR 8181

Submission date :

2024-01-20T00:25:45Z
2024-02-09T16:22:45Z

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Except where otherwise noted, this item's license is described as Attribution 3.0 United States

Version	Link	Modification date
2	20.500.12210/103481*	2024-02-09T16:18:57Z
1	20.500.12210/103481.1	2024-01-20T00:25:45Z

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