The high-throughput highway to computational materials design
Abstract
High-throughput computational materials design is an emerging area of materials science. By combining advanced thermodynamic and electronic-structure methods with intelligent data mining and database construction, and exploiting the power of current supercomputer architectures, scientists generate, manage and analyse enormous data repositories for the discovery of novel materials. In this Review we provide a current snapshot of this rapidly evolving field, and highlight the challenges and opportunities that lie ahead.
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ML methods dependent on large experimental and computational databases are becoming ubiquitous tools for materials development.
“Machine learning methods dependent on large experimental and computational databases, are becoming ubiquitous tools for materials development,198 extending their traditional use for organic molecules.”
From QSAR without borders (2020) · cites this paper as [198]
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References 154 · Cited by 2,053
- Authors
- Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O.
- Affiliations
- Brigham Young University (US)
- University of North Texas (US)
- Duke University (US)
- Venue
- Nature Materials
- Published
- 2013-02-20
- Volume
- 12
- Issue
- 3
- Pages
- 191-201
- ISSN
- 1476-1122 (ISSN-L)
- 1476-4660
- OpenAlex
- W2123306226
- Cited by
- 2,053
- References
- 154
- Citations by year
- 2026: 175 · 2025: 169 · 2024: 172 · 2023: 147 · 2022: 172 · 2021: 176 · 2020: 205 · 2019: 202 · 2018: 180 · 2017: 119 · 2016: 133 · 2015: 85 · 2014: 82 · 2013: 35
- FWCI
- 50.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Materials (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- materials discovery, thermodynamic method, electronic structure methods, data mining, materials database, high-performance computing, materials informatics
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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.
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