Machine‐Learning Approaches to Tune Descriptors and Predict the Viscosities of Ionic Liquids and Their Mixtures

Bibliographic Details
Main Author: Carrera, Gonçalo V. S. M.
Publication Date: 2021
Other Authors: Nunes da Ponte, Manuel
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/117268
Summary: PTDC/EQU-EQU/30060/2017 UIDB/50006/2020
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spelling Machine‐Learning Approaches to Tune Descriptors and Predict the Viscosities of Ionic Liquids and Their Mixtureschemoinformaticsionic liquidsKohonen neural networkrandom forestviscosityElectrochemistrySpectroscopyCatalysisFluid Flow and Transfer ProcessesPTDC/EQU-EQU/30060/2017 UIDB/50006/2020This work consists on a new chemoinformatic approach based on two complementary artificial intelligence concepts. Random Forest and Kohonen neural network are applied on this context. The former provides a relevance measure of the numerical descriptors encoding either an ionic liquid or its mixtures. The code of a given chemical system is weighted according that relevance measure. The Kohonen neural network is trained with a set of weighted chemical systems. The next step comprises the use of the trained neural network as platform to obtain a tuned profile of numerical descriptors representing a generical chemical system. The tuning mechanism involves the topology of a chemical system‐encoding vector in the neural network. The last step comprises the use of the tuned chemical systems to build predictive models of viscosities. The MOLMAP encoding technology is applied to represent ionic liquid systems and its mixtures.LAQV@REQUIMTERUNCarrera, Gonçalo V. S. M.Nunes da Ponte, Manuel2021-05-06T22:43:47Z2021-052021-05-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10application/pdfhttp://hdl.handle.net/10362/117268engPURE: 26884947https://doi.org/10.1002/cmtd.202000031info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-04-14T01:36:40Zoai:run.unl.pt:10362/117268Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:24:08.134164Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Machine‐Learning Approaches to Tune Descriptors and Predict the Viscosities of Ionic Liquids and Their Mixtures
title Machine‐Learning Approaches to Tune Descriptors and Predict the Viscosities of Ionic Liquids and Their Mixtures
spellingShingle Machine‐Learning Approaches to Tune Descriptors and Predict the Viscosities of Ionic Liquids and Their Mixtures
Carrera, Gonçalo V. S. M.
chemoinformatics
ionic liquids
Kohonen neural network
random forest
viscosity
Electrochemistry
Spectroscopy
Catalysis
Fluid Flow and Transfer Processes
title_short Machine‐Learning Approaches to Tune Descriptors and Predict the Viscosities of Ionic Liquids and Their Mixtures
title_full Machine‐Learning Approaches to Tune Descriptors and Predict the Viscosities of Ionic Liquids and Their Mixtures
title_fullStr Machine‐Learning Approaches to Tune Descriptors and Predict the Viscosities of Ionic Liquids and Their Mixtures
title_full_unstemmed Machine‐Learning Approaches to Tune Descriptors and Predict the Viscosities of Ionic Liquids and Their Mixtures
title_sort Machine‐Learning Approaches to Tune Descriptors and Predict the Viscosities of Ionic Liquids and Their Mixtures
author Carrera, Gonçalo V. S. M.
author_facet Carrera, Gonçalo V. S. M.
Nunes da Ponte, Manuel
author_role author
author2 Nunes da Ponte, Manuel
author2_role author
dc.contributor.none.fl_str_mv LAQV@REQUIMTE
RUN
dc.contributor.author.fl_str_mv Carrera, Gonçalo V. S. M.
Nunes da Ponte, Manuel
dc.subject.por.fl_str_mv chemoinformatics
ionic liquids
Kohonen neural network
random forest
viscosity
Electrochemistry
Spectroscopy
Catalysis
Fluid Flow and Transfer Processes
topic chemoinformatics
ionic liquids
Kohonen neural network
random forest
viscosity
Electrochemistry
Spectroscopy
Catalysis
Fluid Flow and Transfer Processes
description PTDC/EQU-EQU/30060/2017 UIDB/50006/2020
publishDate 2021
dc.date.none.fl_str_mv 2021-05-06T22:43:47Z
2021-05
2021-05-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/117268
url http://hdl.handle.net/10362/117268
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 26884947
https://doi.org/10.1002/cmtd.202000031
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 10
application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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