Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
Open accessAbstract
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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- Authors
- Agrawal, A., & Choudhary, A.
- Affiliations
- Northwestern University (US)
- Venue
- APL Materials
- Publisher
- AIP Publishing
- 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
- gold
- doi.org
- 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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