Exploring correlations between properties using artificial neural networks
Exploring correlations between properties using artificial neural networks
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.
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Zhang, Yiming
2cb04c53-2bb8-4482-9c6a-88b55533b41a
Evans, Julian R. G.
1e4ff64d-fdc6-460e-ae9f-f41746899f57
Yang, Shoufeng
e0018adf-8123-4a54-b8dd-306c10ca48f1
Zhang, Yiming
2cb04c53-2bb8-4482-9c6a-88b55533b41a
Evans, Julian R. G.
1e4ff64d-fdc6-460e-ae9f-f41746899f57
Yang, Shoufeng
e0018adf-8123-4a54-b8dd-306c10ca48f1
Zhang, Yiming, Evans, Julian R. G. and Yang, Shoufeng
(2019)
Exploring correlations between properties using artificial neural networks.
Metallurgical and Materials Transactions A, .
(doi:10.1007/s11661-019-05502-8).
Abstract
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.
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Accepted/In Press date: 30 October 2019
e-pub ahead of print date: 30 October 2019
Identifiers
Local EPrints ID: 435659
URI: http://eprints.soton.ac.uk/id/eprint/435659
ISSN: 1073-5623
PURE UUID: dae4d227-adc5-4929-85f4-be463720e924
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Date deposited: 18 Nov 2019 17:30
Last modified: 16 Mar 2024 05:14
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Author:
Yiming Zhang
Author:
Julian R. G. Evans
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