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Towards machine learning approaches for predicting the self-healing efficiency of materials

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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.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.date.accessioned2019-06-24T09:30:07Z
dc.date.available2019-06-24T09:30:07Z
dc.date.issued2019-10
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.description.statusPeer revieweden
dc.format.extent8
dc.format.extent2548958
dc.identifier144613827
dc.identifier67afc94e-c508-4329-b05e-eb570fb08705
dc.identifier85067405541
dc.identifier85067405541
dc.identifier000475556000023
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.doi10.1016/j.commatsci.2019.05.050
dc.identifier.issn0927-0256
dc.identifier.otherMendeley: 028e487f-087b-3b4c-93aa-7d4eb02c7f23
dc.identifier.otherORCID: /0000-0002-1761-6659/work/59923282
dc.identifier.urihttp://hdl.handle.net/2164/12426
dc.identifier.urlhttp://www.mendeley.com/research/towards-machine-learning-approaches-predicting-selfhealing-efficiency-materialsen
dc.identifier.vol168en
dc.language.isoeng
dc.relation.ispartofComputational Materials Scienceen
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.subjectGeneral Computer Scienceen
dc.subjectGeneral Chemistryen
dc.subjectGeneral Materials Scienceen
dc.subjectMechanics of Materialsen
dc.subjectGeneral Physics and Astronomyen
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

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