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Informatics for combinatorial materials science

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  • Materials Informatics 2008
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Abstract

Combinatorial experiments aim to create large amounts of data and information, and managing that data is a challenge. This article will focus on how to scientifically interpret the data generated from combinatorial experiments and high-throughput screening.

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Authors and Affiliations

  1. Institute for Combinatorial Discovery and the NSF Combinatorial Sciences Materials Informatics Collaboratory-International Materials Institute at Iowa State University, Ames, IA, 50011, USA

    S. Broderick, C. Suh, J. Nowers, S. Mallapragada, B. Narasimhan & K. Rajan

  2. Department of Chemical Engineering, Bucknell University, Lewisburg, Pennsylvania, USA

    B. Vogel

Authors
  1. S. Broderick
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  2. C. Suh
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  3. J. Nowers
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  4. B. Vogel
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  5. S. Mallapragada
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  6. B. Narasimhan
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  7. K. Rajan
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Corresponding author

Correspondence to K. Rajan.

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Broderick, S., Suh, C., Nowers, J. et al. Informatics for combinatorial materials science. JOM 60, 56–59 (2008). https://doi.org/10.1007/s11837-008-0035-x

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  • DOI: https://doi.org/10.1007/s11837-008-0035-x

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