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

Engineering and Technology

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

K. Li, D. Persaud, et al.

Discover groundbreaking research by Kangming Li, Daniel Persaud, Kamal Choudhary, Brian DeCost, Michael Greenwood, and Jason Hattrick-Simpers, revealing that up to 95% of materials dataset can be eliminated without sacrificing prediction accuracy. This study challenges conventional wisdom by demonstrating that less can indeed be more when it comes to machine learning datasets.

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