Wang, WenjunMoreau, Nicolette G.Yuan, YingfangRace, Paul R.Pang, Wei2019-06-242019-06-242019-10Wang, 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.2019.05.0500927-0256Mendeley: 028e487f-087b-3b4c-93aa-7d4eb02c7f23ORCID: /0000-0002-1761-6659/work/59923282http://hdl.handle.net/2164/12426Acknowledgement 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.82548958engself-healing efficiencypredictive modelregression and classificationartificial neural networksonline ensemble learning frameworkQA75 Electronic computers. Computer scienceGeneral Computer ScienceGeneral ChemistryGeneral Materials ScienceMechanics of MaterialsGeneral Physics and AstronomyComputational MathematicsEngineering and Physical Sciences Research Council (EPSRC)EP/R020957/1QA75Journal article10.1016/j.commatsci.2019.05.050http://www.mendeley.com/research/towards-machine-learning-approaches-predicting-selfhealing-efficiency-materials168