Applications of machine learning in supercritical fluids research
RIGNANESE, Gian-Marco
Institut de la matière condensée et des nanosciences / Institute of Condensed Matter and Nanosciences [IMCN]
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]
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Institut de Mécanique et d'Ingénierie [I2M]
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]
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]
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Institut de Mécanique et d'Ingénierie [I2M]
Institut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
Language
en
Article de revue
This item was published in
Journal of Supercritical Fluids. 2023, vol. 202, p. 106051
Elsevier
English Abstract
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 ...Read more >
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.Read less <
English Keywords
Machine Learning
Optimization
Supercritical Fluids
Data Intensive Computing
Regression
ANR Project
Sels fondus en milieu hydrothermal pour des degrés d'oxydation élevés du Mn(V) - ANR-21-CE50-0021
Origin
Hal imported