Cited by 1,385Machine Learning in Materials ScienceComputational Drug Discovery MethodsMaterial Selection and Properties

Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science

Open access
Agrawal, Ankit, Choudhary, Alok·2016·APL Materials

Abstract

Our ability to collect “big data” has greatly surpassed our capability to analyze it, underscoring the emergence of the fourth paradigm of science, which is data-driven discovery. The need for data informatics is also emphasized by the Materials Genome Initiative (MGI), further boosting the emerging field of materials informatics. In this article, we look at how data-driven techniques are playing a big role in deciphering processing-structure-property-performance relationships in materials, with illustrative examples of both forward models (property prediction) and inverse models (materials discovery). Such analytics can significantly reduce time-to-insight and accelerate cost-effective materials discovery, which is the goal of MGI.

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Crossref · 10.1063/1.4946894

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70 of 70 references verified0 citation errors · 0 not checkable

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Authors
Agrawal, A., & Choudhary, A.
Affiliations
Northwestern University (US)
Venue
APL Materials
Published
2016-04-15
Volume
4
Issue
5
ISSN
2166-532X (ISSN-L)
OpenAlex
W2338402873
Cited by
1,385
References
70
Citations by year
2026: 247 · 2025: 190 · 2024: 200 · 2023: 161 · 2022: 160 · 2021: 140 · 2020: 105 · 2019: 91 · 2018: 65 · 2017: 17 · 2016: 9
FWCI
26.99
Type
journal article
Language
en
Open access
Locations
  • APL Materials (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institute of Standards and Technology (70NANB14H012)
  • Air Force Office of Scientific Research (FA9550-12-1-0458)
  • Defense Advanced Research Projects Agency (N66001-15-C-4036)
Topics
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Materials Chemistry › Material Selection and Properties
Keywords
materials informatics, big data, data-driven discovery, Materials Genome Initiative, materials discovery, property prediction, inverse model, forward model

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