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Emerging materials intelligence ecosystems propelled by machine learning

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Batra, Rohit, Song, Le, Ramprasad, Rampi·2020·Nature Reviews Materials

DOI 10.1038/s41578-020-00255-y

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See all 471

Authors: Batra, R., Song, L., & Ramprasad, R.
Affiliations: Argonne National Laboratory (US)
Georgia Institute of Technology (US)
Corresponding author: Ramprasad, Rampi
ORCID: Batra, Rohit
Song, Le
Ramprasad, Rampi
Venue: Nature Reviews Materials
Publisher: Springer Science and Business Media LLC
Published: 2020-11-09
Volume: 6
Issue: 8
Pages: 655-678
ISSN: 2058-8437 (ISSN-L)
DOI: 10.1038/s41578-020-00255-y
OpenAlex: W3100445615
Cited by: 446
References: 238
Citations by year: 2026: 109 · 2025: 99 · 2024: 81 · 2023: 56 · 2022: 60 · 2021: 39 · 2020: 2
FWCI: 2.01
Type: journal article
Language: en
Open access: green
www.osti.gov
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Reviews Materials (publishedVersion)
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Funders: National Science Foundation
U.S. Department of Energy
Toyota Research Institute
Office of Naval Research
Topics: Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords: machine learning, human-machine collaboration, materials discovery, materials development, materials informatics

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