dc.contributor.author | Wang, Wenjun | |
dc.contributor.author | Moreau, Nicolette G. | |
dc.contributor.author | Yuan, Yingfang | |
dc.contributor.author | Race, Paul R. | |
dc.contributor.author | Pang, Wei | |
dc.date.accessioned | 2019-06-24T09:30:07Z | |
dc.date.available | 2019-06-24T09:30:07Z | |
dc.date.issued | 2019-10 | |
dc.identifier.citation | Wang , 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.2019.05.050 | en |
dc.identifier.issn | 0927-0256 | |
dc.identifier.other | PURE: 144613827 | |
dc.identifier.other | PURE UUID: 67afc94e-c508-4329-b05e-eb570fb08705 | |
dc.identifier.other | Scopus: 85067405541 | |
dc.identifier.other | Mendeley: 028e487f-087b-3b4c-93aa-7d4eb02c7f23 | |
dc.identifier.other | Scopus: 85067405541 | |
dc.identifier.other | ORCID: /0000-0002-1761-6659/work/59923282 | |
dc.identifier.other | WOS: 000475556000023 | |
dc.identifier.uri | http://hdl.handle.net/2164/12426 | |
dc.identifier.uri | http://www.mendeley.com/research/towards-machine-learning-approaches-predicting-selfhealing-efficiency-materials | en |
dc.description | Acknowledgement 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.extent | 8 | |
dc.language.iso | eng | |
dc.relation.ispartof | Computational Materials Science | en |
dc.rights | https://creativecommons.org/licenses/by/4.0/ | en |
dc.subject | self-healing efficiency | en |
dc.subject | predictive model | en |
dc.subject | regression and classification | en |
dc.subject | artificial neural networks | en |
dc.subject | online ensemble learning framework | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | Computer Science(all) | en |
dc.subject | Chemistry(all) | en |
dc.subject | Materials Science(all) | en |
dc.subject | Mechanics of Materials | en |
dc.subject | Physics and Astronomy(all) | en |
dc.subject | Computational Mathematics | en |
dc.subject | Engineering and Physical Sciences Research Council (EPSRC) | en |
dc.subject | EP/R020957/1 | en |
dc.subject.lcc | QA75 | en |
dc.title | Towards machine learning approaches for predicting the self-healing efficiency of materials | en |
dc.type | Journal article | en |
dc.contributor.institution | University of Aberdeen.Computing Science | en |
dc.contributor.institution | University of Aberdeen.Natural & Computing Sciences | en |
dc.contributor.institution | University of Aberdeen.Institute for Complex Systems and Mathematical Biology (ICSMB) | en |
dc.description.status | Peer reviewed | en |
dc.description.version | Publisher PDF | en |
dc.identifier.doi | https://doi.org/10.1016/j.commatsci.2019.05.050 | |
dc.identifier.url | http://www.mendeley.com/research/towards-machine-learning-approaches-predicting-selfhealing-efficiency-materials | en |
dc.identifier.vol | 168 | en |