Exploiting redundancy in large materials datasets for efficient machine learning with less data

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 machin...

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Detalles Bibliográficos
Autores principales: Li, Kangming, Persaud, Daniel, Choudhary, Kamal, DeCost, Brian, Greenwood, Michael, Hattrick-Simpers, Jason
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638383/
https://www.ncbi.nlm.nih.gov/pubmed/37949845
http://dx.doi.org/10.1038/s41467-023-42992-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638383/
https://www.ncbi.nlm.nih.gov/pubmed/37949845
http://dx.doi.org/10.1038/s41467-023-42992-y

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