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Applications of machine learning in supercritical fluids research

hal.structure.identifierInstitut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
dc.contributor.authorROACH, Lucien
hal.structure.identifierInstitut de la matière condensée et des nanosciences / Institute of Condensed Matter and Nanosciences [IMCN]
dc.contributor.authorRIGNANESE, Gian-Marco
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
hal.structure.identifierInstitut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
dc.contributor.authorERRIGUIBLE, Arnaud
hal.structure.identifierInstitut de Chimie de la Matière Condensée de Bordeaux [ICMCB]
dc.contributor.authorAYMONIER, Cyril
dc.date.accessioned2023-11-20T17:26:34Z
dc.date.available2023-11-20T17:26:34Z
dc.date.issued2023
dc.identifier.issn0896-8446
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/185878
dc.description.abstractEnMachine 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.
dc.description.sponsorshipSels fondus en milieu hydrothermal pour des degrés d'oxydation élevés du Mn(V) - ANR-21-CE50-0021
dc.language.isoen
dc.publisherElsevier
dc.subject.enMachine Learning
dc.subject.enOptimization
dc.subject.enSupercritical Fluids
dc.subject.enData Intensive Computing
dc.subject.enRegression
dc.title.enApplications of machine learning in supercritical fluids research
dc.typeArticle de revue
dc.typeArticle de synthèse
dc.identifier.doi10.1016/j.supflu.2023.106051
dc.subject.halChimie/Chemo-informatique
dc.subject.halInformatique [cs]/Apprentissage [cs.LG]
dc.subject.halInformatique [cs]/Modélisation et simulation
dc.subject.halPhysique [physics]/Mécanique [physics]/Mécanique des fluides [physics.class-ph]
bordeaux.journalJournal of Supercritical Fluids
bordeaux.page106051
bordeaux.volume202
bordeaux.hal.laboratoriesInstitut de Chimie de la Matière Condensée de Bordeaux (ICMCB) - UMR 5026*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-04187846
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04187846v1
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