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      New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

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          Abstract

          Abstract

          Data mining has revolutionized sectors as diverse as pharmaceutical drug discovery, finance, medicine, and marketing, and has the potential to similarly advance materials science. In this paper, we describe advances in simulation-based materials databases, open-source software tools, and machine learning algorithms that are converging to create new opportunities for materials informatics. We discuss the data mining techniques of exploratory data analysis, clustering, linear models, kernel ridge regression, tree-based regression, and recommendation engines. We present these techniques in the context of several materials application areas, including compound prediction, Li-ion battery design, piezoelectric materials, photocatalysts, and thermoelectric materials. Finally, we demonstrate how new data and tools are making it easier and more accessible than ever to perform data mining through a new analysis that learns trends in the valence and conduction band character of compounds in the Materials Project database using data on over 2500 compounds.

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          Most cited references77

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            Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

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              Bond-valence parameters for solids

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                Author and article information

                Journal
                applab
                Journal of Materials Research
                J. Mater. Res.
                Cambridge University Press (CUP)
                0884-2914
                2044-5326
                April 28 2016
                April 1 2016
                : 31
                : 08
                : 977-994
                Article
                10.1557/jmr.2016.80
                416b41ea-2493-4a20-b7b9-fec43578fe81
                © 2016
                History

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