Gradient Boosted Machine Learning Model to Predict H

Carbon Dioxide Metal-Organic Frameworks Biological Transport Algorithms Gases Machine Learning

Journal

Journal of chemical information and modeling
ISSN: 1549-960X
Titre abrégé: J Chem Inf Model
Pays: United States
ID NLM: 101230060

Informations de publication

Date de publication:
14 08 2023
Historique:
medline: 15 8 2023
pubmed: 18 7 2023
entrez: 18 7 2023
Statut: ppublish

Résumé

Predictive screening of metal-organic framework (MOF) materials for their gas uptake properties has been previously limited by using data from a range of simulated sources, meaning the final predictions are dependent on the performance of these original models. In this work, experimental gas uptake data has been used to create a Gradient Boosted Tree model for the prediction of H

Identifiants

DOI: 10.1021/acs.jcim.3c00135 PMID: 37463276 PMC: PMC10428209
pubmed: 37463276
doi: 10.1021/acs.jcim.3c00135
pmc: PMC10428209
doi:

Substances chimiques

Carbon Dioxide 142M471B3J
Metal-Organic Frameworks 0
Gases 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

4545-4551

Références

J Am Chem Soc. 2020 Feb 26;142(8):3814-3822
pubmed: 32017547
J Chem Inf Comput Sci. 2003 Nov-Dec;43(6):1947-58
pubmed: 14632445
Chem Commun (Camb). 2010 Jan 7;46(1):44-53
pubmed: 20024292
ACS Comb Sci. 2017 Oct 9;19(10):640-645
pubmed: 28800219
Nat Commun. 2019 May 28;10(1):2345
pubmed: 31138802
Nat Commun. 2020 Jun 26;11(1):3230
pubmed: 32591514
Chem Soc Rev. 2009 May;38(5):1284-93
pubmed: 19384438
Angew Chem Int Ed Engl. 2008;47(27):4966-81
pubmed: 18459091
Chem Commun (Camb). 2014 May 18;50(38):4911-4
pubmed: 24695743
J Comput Chem. 2018 Oct 30;39(28):2405-2408
pubmed: 30368843
J Anim Ecol. 2008 Jul;77(4):802-13
pubmed: 18397250

Auteurs

Tom Bailey (T)

School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, U.K.
ORCID: 0000-0002-9290-0975

Adam Jackson (A)

School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, U.K.

Razvan-Antonio Berbece (RA)

School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, U.K.

Kejun Wu (K)

School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, U.K.
Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China.

Nicole Hondow (N)

School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, U.K.

Elaine Martin (E)

School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, U.K.

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Classifications MeSH

questionsmedicales.fr › Produits chimiques inorganiques › Composés de l'oxygène › Oxydes › Dioxyde de carbone › Gradient Boosted Machine Learning Model to Predict H
questionsmedicales.fr › Structures macromoléculaires › Réseaux organométalliques › Gradient Boosted Machine Learning Model to Predict H
questionsmedicales.fr › Métabolisme › Transport biologique › Gradient Boosted Machine Learning Model to Predict H
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Gradient Boosted Machine Learning Model to Predict H
questionsmedicales.fr › Produits chimiques inorganiques › Gaz › Gradient Boosted Machine Learning Model to Predict H
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Gradient Boosted Machine Learning Model to Predict H