Exploiting redundancy in large materials datasets for efficient machine learning with less data Article Swipe
Kangming Li
,
Daniel Persaud
,
Kamal Choudhary
,
Brian DeCost
,
Michael T. Greenwood
,
Jason Hattrick‐Simpers
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-023-42992-y
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-023-42992-y
Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multiple large datasets for various material properties, by revealing that up to 95% of data can be safely removed from machine learning training with little impact on in-distribution prediction performance. The redundant data is related to over-represented material types and does not mitigate the severe performance degradation on out-of-distribution samples. In addition, we show that uncertainty-based active learning algorithms can construct much smaller but equally informative datasets. We discuss the effectiveness of informative data in improving prediction performance and robustness and provide insights into efficient data acquisition and machine learning training. This work challenges the “bigger is better” mentality and calls for attention to the information richness of materials data rather than a narrow emphasis on data volume.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41467-023-42992-y
- https://www.nature.com/articles/s41467-023-42992-y.pdf
- OA Status
- gold
- Cited By
- 75
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388567027
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388567027Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41467-023-42992-yDigital Object Identifier
- Title
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Exploiting redundancy in large materials datasets for efficient machine learning with less dataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-11-10Full publication date if available
- Authors
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Kangming Li, Daniel Persaud, Kamal Choudhary, Brian DeCost, Michael T. Greenwood, Jason Hattrick‐SimpersList of authors in order
- Landing page
-
https://doi.org/10.1038/s41467-023-42992-yPublisher landing page
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https://www.nature.com/articles/s41467-023-42992-y.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41467-023-42992-y.pdfDirect OA link when available
- Concepts
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Computer science, Redundancy (engineering), Robustness (evolution), Machine learning, Training set, Artificial intelligence, Data mining, Biochemistry, Chemistry, Operating system, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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75Total citation count in OpenAlex
- Citations by year (recent)
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2025: 39, 2024: 33, 2023: 3Per-year citation counts (last 5 years)
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54Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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