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Title: Applications of Artificial Neural Networks (ANNs) in exploring materials property-property correlations
Author: Cheng, Xiaoyu
ISNI:       0000 0004 5355 3311
Awarding Body: Queen Mary, University of London
Current Institution: Queen Mary, University of London
Date of Award: 2014
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The discoveries of materials property-property correlations usually require prior knowledge or serendipity, the process of which can be time-consuming, costly, and labour-intensive. On the other hand, artificial neural networks (ANNs) are intelligent and scalable modelling techniques that have been used extensively to predict properties from materials’ composition or processing parameters, but are seldom used in exploring materials property-property correlations. The work presented in this thesis has employed ANNs combinatorial searches to explore the correlations of different materials properties, through which, ‘known’ correlations are verified, and ‘unknown’ correlations are revealed. An evaluation criterion is proposed and demonstrated to be useful in identifying nontrivial correlations. The work has also extended the application of ANNs in the fields of data corrections, property predictions and identifications of variables’ contributions. A systematic ANN protocol has been developed and tested against the known correlating equations of elastic properties and the experimental data, and is found to be reliable and effective to correct suspect data in a complicated situation where no prior knowledge exists. Moreover, the hardness increments of pure metals due to HPT are accurately predicted from shear modulus, melting temperature and Burgers vector. The first two variables are identified to have the largest impacts on hardening. Finally, a combined ANN-SR (symbolic regression) method is proposed to yield parsimonious correlating equations by ruling out redundant variables through the partial derivatives method and the connection weight approach, which are based on the analysis of the ANNs weight vectors. By applying this method, two simple equations that are at least as accurate as other models in providing a rapid estimation of the enthalpies of vaporization for compounds are obtained.
Supervisor: Not available Sponsor: School of Engineering and Materials Science, Queen Mary, University of London ; China Scholarship Council (CSC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: artificial neural networks ; materials property-property correlations. ; materials properties ; symbolic regression