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“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]
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“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 .”
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Metadata
- 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
- 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
- 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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