Informatics for combinatorial materials science

S. Broderick; C. Suh; J. Nowers; B. Vogel; S. Mallapragada; B. Narasimhan; K. Rajan

doi:10.1007/s11837-008-0035-x

Informatics for combinatorial materials science

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

Department of Materials Design and Innovation

Iowa State University
Bucknell University

Research output: Contribution to journal › Review article › peer-review

27 Scopus citations

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.

Original language	English
Pages (from-to)	56-59
Number of pages	4
Journal	JOM
Volume	60
Issue number	3
DOIs	https://doi.org/10.1007/s11837-008-0035-x
State	Published - Mar 2008

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10.1007/s11837-008-0035-x

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title = "Informatics for combinatorial materials science",

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.",

author = "S. Broderick and C. Suh and J. Nowers and B. Vogel and S. Mallapragada and B. Narasimhan and K. Rajan",

year = "2008",

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T1 - Informatics for combinatorial materials science

AU - Broderick, S.

AU - Suh, C.

AU - Nowers, J.

AU - Vogel, B.

AU - Mallapragada, S.

AU - Narasimhan, B.

AU - Rajan, K.

PY - 2008/3

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N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/46949091541

U2 - 10.1007/s11837-008-0035-x

DO - 10.1007/s11837-008-0035-x

M3 - Review article

AN - SCOPUS:46949091541

SN - 1047-4838

VL - 60

SP - 56

EP - 59

JO - JOM

JF - JOM

IS - 3

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Informatics for combinatorial materials science

Abstract

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