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

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

Type de document :

Article dans une revue scientifique: Article original

DOI :

10.3390/molecules28196805

PMID :

37836648

URL permanente :

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

Titre :

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

Auteur(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]

Titre de la revue :

Molecules

Nom court de la revue :

Molecules

Numéro :

Date de publication :

2023-10-16

ISSN :

1420-3049

Résumé en anglais : [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, ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CNRS
Centrale Lille
ENSCL
Univ. Artois

Collections :

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

Date de dépôt :

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

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molecules-28-06805-v2.pdf
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Ce document est diffusé sous licence Attribution 3.0 United States

Numéro de version	Lien	Date de modification
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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