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

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)180-187
Number of pages8
JournalComputational Materials Science
Volume168
Early online date19 Jun 2019
DOIs
DateAccepted/In press - 25 May 2019
DateE-pub ahead of print - 19 Jun 2019
DatePublished (current) - 1 Oct 2019

Abstract

Self-healing materials with an inherent repair mechanism have been widely studied. However, the self-healing efficiencies of most materials can only be measured by laboratory-based experiments, which can be time consuming and expensive. Inspired by modern machine learning approaches, we are interested in predicting the self-healing efficiency of new bio-hybrid materials, as part of our ongoing EPSRC funded ’Manufacturing Immortality’ project. By modelling existing experimental data, predictive models can be built to forecast self-healing efficiency. This has the potential to reduce the time input required by laboratory experiments, guide material and component selection, and inform hypotheses, thereby facilitating the design of novel self-healing materials. In this position paper, we first present preliminary knowledge and quantitative definitions of the self-healing efficiency of materials. We then demonstrate several widely used machine learning approaches and review an experimental case of predictive modelling based on neural networks. Furthermore, and aiming to expedite self-healing material development, we propose an on-line ensemble learning framework as the whole system model for the optimization of predictive computational models. Finally, the rationality of our on-line ensemble learning framework is experimentally studied and validated

    Research areas

  • Artificial neural network, Online ensemble learning framework, Predictive model, Regression and classification, Self-healing efficiency, Synthetic Biology

    Structured keywords

  • Bristol BioDesign Institute

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    Rights statement: This is the final published version of the article (version of record). It first appeared online via Elsevier at https://doi.org/10.1016/j.commatsci.2019.05.050 . Please refer to any applicable terms of use of the publisher.

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