Visualization in Materials Research

Bryden, Aaron; Rajan, Krishna; LeSar, Richard

doi:10.1016/b978-0-12-394399-6.00007-2

Informatics for Materials Science and Engineering 2013

DOI: 10.1016/b978-0-12-394399-6.00007-2

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Visualization in Materials Research

Aaron Bryden

Krishna Rajan

Richard LeSar

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Cited by 2 publications

References 32 publications

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Data analytics and parallel-coordinate materials property charts

Rickman

2018

npj Comput Mater

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It is often advantageous to display material properties relationships in the form of charts that highlight important correlations and thereby enhance our understanding of materials behavior and facilitate materials selection. Unfortunately, in many cases, these correlations are highly multidimensional in nature, and one typically employs low-dimensional cross-sections of the property space to convey some aspects of these relationships. To overcome some of these difficulties, in this work we employ methods of data analytics in conjunction with a visualization strategy, known as parallel coordinates, to represent better multidimensional materials data and to extract useful relationships among properties. We illustrate the utility of this approach by the construction and systematic analysis of multidimensional materials properties charts for metallic and ceramic systems. These charts simplify the description of high-dimensional geometry, enable dimensional reduction and the identification of significant property correlations and underline distinctions among different materials classes. INTRODUCTIONThe mapping of material properties, pioneered by Ashby and coworkers, leads to the creation of charts that condense a large quantity of information into a useful representation that has the virtue of revealing important property correlations and that facilitates materials selection and design.1,2 These (usually twodimensional) charts form the basis of an optimized, systematic methodology for materials selection based on materials informatics. In particular, this program involves the identification of technical design requirements and the associated materials indices used for materials selection to meet these requirements.

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Data analytics and parallel-coordinate materials property charts

Rickman

2018

npj Comput Mater

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Clustering Algorithms: An Application for Adsorption Kinetic Curves

Rendón

Alejo

Rivas

2021

IEEE Latin Am. Trans.

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Visualization in Materials Research

Cited by 2 publications

References 32 publications

Data analytics and parallel-coordinate materials property charts

Data analytics and parallel-coordinate materials property charts

Clustering Algorithms: An Application for Adsorption Kinetic Curves

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