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dc.contributor.authorWang, Wenjun
dc.contributor.authorMoreau, Nicolette G.
dc.contributor.authorYuan, Yingfang
dc.contributor.authorRace, Paul R.
dc.contributor.authorPang, Wei
dc.date.accessioned2019-06-24T09:30:07Z
dc.date.available2019-06-24T09:30:07Z
dc.date.issued2019-10
dc.identifier.citationWang , W , Moreau , N G , Yuan , Y , Race , P R & Pang , W 2019 , ' Towards machine learning approaches for predicting the self-healing efficiency of materials ' , Computational Materials Science , vol. 168 , pp. 180-187 . https://doi.org/10.1016/j.commatsci.2019.05.050en
dc.identifier.issn0927-0256
dc.identifier.otherPURE: 144613827
dc.identifier.otherPURE UUID: 67afc94e-c508-4329-b05e-eb570fb08705
dc.identifier.otherScopus: 85067405541
dc.identifier.otherMendeley: 028e487f-087b-3b4c-93aa-7d4eb02c7f23
dc.identifier.otherScopus: 85067405541
dc.identifier.otherORCID: /0000-0002-1761-6659/work/59923282
dc.identifier.otherWOS: 000475556000023
dc.identifier.urihttp://hdl.handle.net/2164/12426
dc.identifier.urihttp://www.mendeley.com/research/towards-machine-learning-approaches-predicting-selfhealing-efficiency-materialsen
dc.descriptionAcknowledgement This research is supported by the Engineering and Physical Sciences Research Council (EPSRC) funded Project on New Industrial Systems: Manufacturing Immortality (EP/R020957/1). The authors are also grateful to the Manufacturing Immortality consortium.en
dc.format.extent8
dc.language.isoeng
dc.relation.ispartofComputational Materials Scienceen
dc.rightshttps://creativecommons.org/licenses/by/4.0/en
dc.subjectself-healing efficiencyen
dc.subjectpredictive modelen
dc.subjectregression and classificationen
dc.subjectartificial neural networksen
dc.subjectonline ensemble learning frameworken
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectComputer Science(all)en
dc.subjectChemistry(all)en
dc.subjectMaterials Science(all)en
dc.subjectMechanics of Materialsen
dc.subjectPhysics and Astronomy(all)en
dc.subjectComputational Mathematicsen
dc.subjectEngineering and Physical Sciences Research Council (EPSRC)en
dc.subjectEP/R020957/1en
dc.subject.lccQA75en
dc.titleTowards machine learning approaches for predicting the self-healing efficiency of materialsen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Natural & Computing Sciencesen
dc.contributor.institutionUniversity of Aberdeen.Institute for Complex Systems and Mathematical Biology (ICSMB)en
dc.description.statusPeer revieweden
dc.description.versionPublisher PDFen
dc.identifier.doihttps://doi.org/10.1016/j.commatsci.2019.05.050
dc.identifier.urlhttp://www.mendeley.com/research/towards-machine-learning-approaches-predicting-selfhealing-efficiency-materialsen


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