Exploring Correlations Between Properties Using Artificial Neural Networks
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Exploring Correlations Between Properties Using Artificial Neural Networks YIMING ZHANG, JULIAN R.G. EVANS, and SHOUFENG YANG The traditional aim of materials science is to establish the causal relationships between composition, processing, structure, and properties with the intention that, eventually, these relationships will make it possible to design materials to meet specifications. This paper explores another approach. If properties are related to structure at different scales, there may be relationships between properties that can be discerned and used to make predictions so that knowledge of some properties in a compositional field can be used to predict others. We use the physical properties of the elements as a dataset because it is expected to be both extensive and reliable and we explore this method by showing how it can be applied to predict the polarizability of the elements from other properties. https://doi.org/10.1007/s11661-019-05502-8 The Author(s) 2019
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INTRODUCTION
THE discovery of correlations between datasets has led to many important findings historically[1–3] but there are two essential prerequisites: reliable data and an inspired guess at where to look for correlations. The increase in data handling capacity and advances in intelligent search methods could, it is claimed,[4] change the way in which some sectors of science proceed. Large databases in materials science could make it possible to search for correlations between properties that would not normally be sought. At present, researchers tend to focus on one set of properties in which they are expert rather than connecting one property with another. The traditional methodological framework for materials science is the identification of the composition-processing-structure-properties causal pathways from which many of the successes in materials science have emerged. Once these relationships are in place, it is thought, it will be possible both to understand why existing materials behave as they do and to predict how materials can be chosen and modified to behave as we want. However,
YIMING ZHANG is with the Engineering Laboratory of Advanced Energy Materials, Ningbo Institute of Materials Technology & Engineering, Chinese Academy of Sciences, Ningbo 315201, Zhejiang, P.R. China. JULIAN R.G. EVANS is with the Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK. SHOUFENG YANG is with University of Southampton, University Road, Southampton, SO17 1BJ, UK. Contact e-mail: [email protected] Manuscript submitted April 25, 2019.
METALLURGICAL AND MATERIALS TRANSACTIONS A
the quantitative prediction of properties from the structure is very complex partly because many different scales must be considered and partly because intrinsic and extrinsic imperfections must be taken into account as well. The ‘‘high throughput’’ or ‘‘combinatorial’’ methods are an attempt to increase the pace of materials development in increasingly complex compositional spaces.[5] Combinatorial libraries can be regarded as a capita
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