ROACH, Lucien; RIGNANESE, Gian-Marco; ERRIGUIBLE, Arnaud; AYMONIER, Cyril

doi:10.1016/j.supflu.2023.106051

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Licence d’utilisation du document

ROACH, Lucien
Institut de Chimie de la Matière Condensée de Bordeaux [ICMCB]

RIGNANESE, Gian-Marco
Institut de la matière condensée et des nanosciences / Institute of Condensed Matter and Nanosciences [IMCN]

ERRIGUIBLE, Arnaud
Institut de Mécanique et d'Ingénierie [I2M]
Institut de Chimie de la Matière Condensée de Bordeaux [ICMCB]

Langue

Article de revue

Ce document a été publié dans

Journal of Supercritical Fluids. 2023, vol. 202, p. 106051

Elsevier

Résumé en anglais

Machine learning has seen increasing implementation as a predictive tool in the chemical and physical sciences in recent years. It offers a route to accelerate the process of scientific discovery through a computational data-driven approach. Whilst machine learning is well established in other fields, such as pharmaceutical research, it is still in its infancy in supercritical fluids research, but will likely accelerate dramatically in coming years. In this review, we present a basic introduction to machine learning and discuss its current uses by supercritical fluids researchers. In particular, we focus on the most common machine learning applications; including: (1) The estimation of the thermodynamic properties of supercritical fluids. (2) The estimation of solubilities, miscibilities, and extraction yields. (3) Chemical reaction optimization. (4) Materials synthesis optimization. (5) Supercritical power systems. (6) Fluid dynamics simulations of supercritical fluids. (7) Molecular simulation of supercritical fluids and (8) Geosequestration of CO2 using supercritical fluids.< Réduire

Mots clés en anglais

Machine Learning

Optimization

Supercritical Fluids

Data Intensive Computing

Regression

URI

https://oskar-bordeaux.fr/handle/20.500.12278/185878

DOI

http://dx.doi.org/10.1016/j.supflu.2023.106051

Project ANR

Sels fondus en milieu hydrothermal pour des degrés d'oxydation élevés du Mn(V) - ANR-21-CE50-0021

Origine

Importé de hal

Unités de recherche

Institut de Chimie de la Matière Condensée de Bordeaux (ICMCB) - UMR 5026