Emerging materials intelligence ecosystems propelled by machine learning
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ML models purely built on data are often agnostic of the underlying physical processes, which leads to inability to produce physically consistent results with limited available training data.
“ML models purely built on data are often agnostic of the underlying physical processes (e.g., the process-structure-property-performance relationships), which leads to inability to produce physically consistent results with limited available training data 133,134 .”
From Machine learning in concrete science: applications, challenges, and best practices (2022) · cites this paper as [134]
NLP offers the opportunity to extract materials data from millions of documents in an automated and more efficient manner compared with manual collection, and thus establish large datasets that can leverage ML techniques.
“This offers the opportunity for NLP to extract materials data from millions of documents in an automated and more efficient manner, compared with the manual collection, and thus establish large datasets that can leverage the power of ML techniques 134 .”
From Machine learning in concrete science: applications, challenges, and best practices (2022) · cites this paper as [134]
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Research works printed but not linked: 12
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Cited by
Sorted by citations
Hart, G. L. W., Mueller, T., Toher, C., & Curtarolo, S. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00340-w · Cited by 703 · see citing works
Machine learning in concrete science: applications, challenges, and best practices
Li, Z., Yoon, J., Zhang, R., Rajabipour, F., Srubar III, W. V., Dabo, I., & Radlińska, A. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00810-x · Cited by 388 · see citing works
Artificial intelligence-powered electronic skin
Xu, C., Solomon, S. A., & Gao, W. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00760-z · Cited by 259 · see citing works
Innovative Materials Science via Machine Learning
Gao, C., Min, X., Fang, M., Tao, T., Zheng, X., Liu, Y., Wu, X., & Huang, Z. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202108044 · Cited by 198 · see citing works
Toward an AI Era: Advances in Electronic Skins
Fu, X., Cheng, W., Wan, G., Yang, Z., & Tee, B. C. K. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00049 · Cited by 172 · see citing works
Zhao, R., Xing, B., Mu, H., Fu, Y., & Zhang, L. (2022) · Chinese Physics B · doi:10.1088/1674-1056/ac5d2d · Cited by 21 · see citing works
- Authors
- Batra, R., Song, L., & Ramprasad, R.
- Affiliations
- Argonne National Laboratory (US)
- Georgia Institute of Technology (US)
- Corresponding author
- Ramprasad, Rampi
- Venue
- Nature Reviews Materials
- Published
- 2020-11-09
- Volume
- 6
- Issue
- 8
- Pages
- 655-678
- ISSN
- 2058-8437 (ISSN-L)
- OpenAlex
- W3100445615
- Cited by
- 446
- References
- 237
- 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
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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